Should You Use Power BI for Predictive Analytics?

How risky is that business loan? Should your company add another branch in a new location? When will your stakeholders make a return on their investments in your company? Predictive data analysis answers questions like these, using historical data to make reliable forecasts.

To make the most accurate predictions, you’ll need a powerful statistical data analysis tool. Power BI for predictive analytics lets you assess trends and forecasts visually for a glimpse into your company’s future.

Can You Use Power BI for Predictive Analytics?

Predictive analytics is the process of analyzing big data, identifying patterns, and producing models that forecast future outcomes. There are many ways to perform predictive analytics. You could hire a statistical data scientist to manually create predictive models, or download predictive analytics software that uses machine learning algorithms to automatically process your data. 

But can you use visualization software like Power BI for predictive analytics as well? The answer is slightly complicated.

Power BI is a visualization platform for displaying your data in graphic form. It’s mainly used to generate visually-compelling reports and to look at your data from multiple perspectives. Its predictive modeling capabilities are pretty limited, particularly if you’re only using a standard Power BI Pro or Premium license and no other tools. Power BI cannot perform any predictive analysis on its own. It can only display the data it’s given.

However, Power BI can be adapted to perform advanced predictive analytics. You just have to connect it with a more robust analytics tool first. Once you have a comprehensive predictive analytics system up and running, you’ll be able to make very accurate models from your data and present them in stunning visual form.

How to Use Power BI for Predictive Analytics 

If you want to use Power BI for predictive analytics, you must link your Power BI account to another predictive data analytics tool. One of the easiest ways to do this is to use Power BI and Microsoft’s Azure software in tandem.

Azure is a cloud computing system that stores and analyzes big data. The platform includes multiple tools, one of which is Azure Machine Learning Studio. This tool uses machine learning algorithms to identify patterns and make predictions about future trends. It also learns from your past data to make more reliable forecasts every time you use the system. To tie an Azure account to a Power BI account, you can:

  1. Connect Azure to your data source or store your data in the Azure system
  2. Drag your data sets or modules into Azure’s predictive model creator
  3. Give Power BI permission to access the models and send this data through Power BI
  4. Generate visuals of the models using one or more of the visuals available in the Power BI gallery.

You are essentially analyzing the data twice. During the first round, Azure runs the data through machine learning algorithms based on the type of predictions you want to make (e.g. budget forecasts or market projections). Once this process is complete, you’re left with a new set of data like any other you would upload to Power BI. The only difference is that this data is predictive rather than historical.

What makes this system so effective is that it combines the power of predictive modeling with user-friendly visual aids. Most laypeople struggle to read and understand complicated predictive models. They need charts or other graphics to help them make sense of the information. Power BI offers some of the most compelling, detailed, and polished visuals on the market. When you use Power BI, your predictions will not only be trustworthy, but they will also be easy to share with your entire organization.

Is Power BI and Azure the Best Predictive Analytics System? 

Although a combination of Azure and Power BI for predictive analytics is a dependable system, it may not be the best option for every organization. The main problem with using Power BI for predictive analytics is that someone on your team will need to be familiar with the R programming language and the Azure platform.

First, you have to access your data from Azure SQL and then use R to extract this data. Next, you must send this data to the Azure ML web service so that it can score the data. From here, you’ll send the data back through Azure SQL and use R once again to send this data to Power BI’s system. Once you’ve reached this point, just publish the data to Power BI and refresh the data through the gateway. Now all that’s left to do is to generate visuals.

If you’re unfamiliar with R programming and Azure,this process is far from simple. It’s also quite time-consuming, even for those who know their way around R and Azure. You’ll have to repeat the entire process for every new prediction or model before you can start generating visuals and reports. This is why many organizations choose to hire third-party predictive analytics experts to build user-friendly systems customized to their workflows and needs.

The Benefits of a Custom Predictive Analytics System 

A custom predictive analytics system can automatically perform all of the tasks that Azure normally would, generating accurate models based on your historical data. To make this system, data experts ask which types of models you’d like to create and run your data through machine learning algorithms designed for those specific tasks. All of the predictive modeling is done for you. There’s no need to learn R commands or practice using the Azure platform.

These firms can also handle visualizations. Experts will build you a user-friendly dashboard where you can quickly generate Power BI visuals or other detailed graphics and charts at the touch of a button. Because your predictive models are already connected to this dashboard, making new visuals is lightning-fast.

Predictive analytics models are only useful if you keep them current and use them in your reports consistently. If they’re too difficult or time-consuming to create, then your managerial team will be less likely to create them— or to consult them when they make their decisions. By outsourcing this process to the experts, you’ll build a system anyone can use, with the most innovative predictive technology.

Do you want to make the most accurate business decisions possible? If so, contact Tek Leaders today. Our team of skilled predictive data analytics and machine learning experts will help you design an efficient system for modeling your data and creating compelling visuals to support it. If you want to learn more about using Power BI for predictive analytics, you can reach us by email directly.

Author: Devender (Dev) Aerrabolu

Devender (Dev) Reddy Aerrabolu is the CEO of Tek Leaders. His goal is to help SMBs bring value from their data. Dev helped Tek Leaders grow from scratch into a $25 million enterprise by focusing on clients’ data needs.

October 15, 2019

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Data Analytics Outsourcing Companies: What To Look For

Data analytics outsourcing companies let even the smallest businesses compete with the biggest names in their industries. These companies offer the same powerful analytics tools that the most successful companies use to get ahead—often at a much lower cost.

Before you hire one of these companies to help you make better data-driven decisions, you’ll need to know what to look for. What sets one trustworthy data analytics company apart from another? In this guide, we’ll show you how to find a third-party firm that will give you the best possible results.

What Does a Data Analytics Outsourcing Company Do? 

Data analytics outsourcing companies take on most or all of the responsibilities of data management for your organization. They usually have their own servers and other hardware, as well as software licenses and proprietary systems for analyzing data. The company charges your business a flat fee to use its system and services.

These services may include:

  • Business intelligence planning and reporting;
  • Data collection and storage (including cloud storage);
  • Big data normalization;
  • Data mining;
  • Predictive analytics (sometimes using machine learning algorithms);
  • Consumer analytics and customer insights;
  • Fraud prevention analytics (particularly for the insurance industry);
  • Spatial analytics;
  • Visualization tools; and more.

Every data analytics outsourcing company is different. Some offer only a few of these benefits, while others cover them all. The cost of services also varies from firm to firm. To choose the right data analytics firm for your organization, think carefully about the benefits they offer and weigh them against the total cost.

How to Choose the Best Data Analytics Outsourcing Company 

How can you tell whether a data analytics outsourcing company is trustworthy and offers the best services for the price? Start by asking your main point of contact these 10 questions:

  • Does my organization need to do anything to prepare for this transition?

The best data analytics outsourcing companies won’t require you to have any existing infrastructure or in-house IT staff. The third-party firm should provide all of the tools and support needed to migrate to new systems.

  • Will my staff need training?

Reliable companies provide all of the training materials for your in-house staff. The best firms also create individual user authorizations and dashboards based on existing workflows. The system should be intuitive from the start.

  • What data storage options does your company offer?

The company must provide secure cloud storage, at minimum. Some companies also allow you to store data onsite and use cloud storage backups. No matter which option you choose, the company should help you migrate to cloud storage from your current system.

  • Is the system flexible?

Customizable dashboards and business intelligence (BI) portals are a must-have service. So are customizable reports and visualizations. Great companies will create a system specifically for your business based on your strategy and BI roadmap.

  • How advanced are the data analytics services and tools?

Services like automated report generation, robust visualizations, and predictive analytics via machine learning are incredibly powerful tools for finding trends in large sets of data. The company you partner with should use the most advanced tools on the market.

  • How reliable is the system?

The third-party firm should provide 24/7 customer support every day of the week to answer any questions your staff may have and to troubleshoot tech problems. It should also guarantee at least 99 percent uptime.

  • Is my data secure?

The company must follow data security best practices, including encryption and robust user authorization protocols.

  • Is the system scalable?

Ask whether you can quickly scale up or down based on your business’ latest needs and market trends.

  • How much does it cost?

The service should be value-driven. In other words, the company will assess the cost of these services based on overall results, not on time or resources spent per project. The best companies charge a fixed monthly fee and only charge for the services you actually use.

  • Can I see examples of dashboards or reports you’ve created in the past?

Seeing real examples of dashboards the company has created will help you decide whether the service is right for you.

Your contact at the data analytics outsourcing company should have a solid answer for each of these questions. If the company doesn’t provide one or more of the key services above, then it may not be worth the investment. When it comes to choosing the best firm to work with, you can afford to be picky. You shouldn’t settle for an outsourcing contract that doesn’t meet all of your needs.

Should You Hire a Third-Party Firm? 

Most businesses, especially small or medium-sized enterprises, should absolutely hire a data analytics outsourcing company. Smaller enterprises often don’t have room in the budget to hire an experienced team of data scientists to perform advanced data analysis every day. By outsourcing this process, you’ll get the same expertise at a much lower cost. You can easily compete with the giants of your industry.

However, not all data analytics outsourcing companies are equally knowledgeable or reliable. If the firm outsources work to unskilled freelancers who have little experience with data science, then you may not get the deep, insightful results you want. Security is another pain point when you hire a third-party firm. If the firm doesn’t take the steps necessary to protect your data, you may be vulnerable to breaches or data theft. Trustworthy firms address all of these issues by hiring the most skilled and experienced data scientists to keep your information safe.

A reliable firm will also work with your current IT staff (if you already have an IT team) to make sure everyone is on the same page. Sometimes, in-house IT staff are hesitant to let another company take over data analysis responsibilities. However, by working closely with your staff, a third-party IT firm can quickly establish trust, prepare your team for the transition, and ease your staff’s workload. Your in-house staff will appreciate having extra time to work on big picture projects by outsourcing tedious data entry and analysis tasks to a third party.

As long as you select the most experienced firm, negotiate a favorable contract, and communicate with your in-house staff, you’ll be successful. By the end of this process, you’ll have a seamless analytics system that generates powerful insights and helps drive your business forward.

If you’re thinking about outsourcing your data analytics process to a third party, contact Tek Leaders today. We are a leading provider of advanced data analytics and business intelligence services. Tek Leaders provides powerful tools and full-service support to businesses looking to maximize their data. If you have more questions about data analytics outsourcing companies,  you can reach us by email directly.

    Author: Devender (Dev) Aerrabolu

    Devender (Dev) Reddy Aerrabolu is the CEO of Tek Leaders. His goal is to help SMBs bring value from their data. Dev helped Tek Leaders grow from scratch into a $25 million enterprise by focusing on clients’ data needs.

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    The Best Dashboard Database Design for Your Company

    You’re driving down a long stretch of highway and aren’t sure whether you should refuel now or at the next gas station about 30 miles away. You glance down at your car’s fuel gauge and see that you still have enough gas to get you through the next 50 miles, at least. So, you decide to drive on.

    Database dashboards work in the same way as vehicle dashboards. They display the most important information right up front so that you can make the best decisions.

    However, unlike the dashboard of your car, your company’s dashboard database design is much more complex. In some cases, you have to display hundreds of numbers and dozens of metrics on a single dashboard to get the most accurate view of what’s happening.

    Effective dashboard database design distills all of this complicated information down to its simplest and most visually-engaging form. There are six design principles that will help you get the most out of your dashboard:

    1. Base your design on business-related questions. 
    2. Design for your end-user or audience. 
    3. Select the most accurate visuals
    4. Tell a story in the design. 
    5. Keep the graphic elements simple. 
    6. Use color to your advantage.

    By following these six tips, you’ll create a beautiful and interactive dashboard database design to drive all of your future decisions.

    How to Get the Best Dashboard Database Design

    Tip #1: Ask Questions Upfront

    One of the biggest mistakes that companies make when they create dashboards is that they try to cram too much information onto a single page. This results in an overly-complicated dashboard that doesn’t tell you what steps you need to take to make your business more successful.

    To prevent this, start by asking what you want to achieve with your dashboard database design. Asking business-related questions helps you to drill down to what’s important. Your dashboard will answer these very specific questions, rather than attempting to cover everything all at once.


    1. How many new customers did we get this month? How did this compare to last month?
    2. What are our top 10 products? Why are these products so popular? 
    3. How is our marketing team performing? How do we improve performance?

    The more specific the questions, the more effective your dashboard database design will be. You’ll see exactly which metrics to track to get the right answers.

    Tip #2: Design for Your Audience 

    Once you have a list of business-related questions you’d like to answer with your dashboard database design, consider who is going to look at the information. Is your audience a group of employees or your CFO? They’ll want to see different things in the design.

    For example, upper management and stakeholders likely want to see big-picture figures that will help them steer the company in the right direction. If you include daily operational metrics in the dashboard design, you’ll only muddy the waters and make their jobs more difficult.

    Likewise, your employees want to know what they should be doing differently to reach their individual goals. If the dashboard is too broad in scope, they’ll struggle to see their roles in the company’s overarching strategy.

    To decide on the right dashboard database design for your audience, choose one of the three most popular dashboard styles:

    • Strategic dashboards for tracking individual goals and Key Performance Indicators (KPIs).
    • Operational dashboards for managers to monitor systematic efficiency, like time to market or equipment maintenance records. 
    • Analytical dashboards for tracking larger market trends and big picture KPIs that stakeholders want to see.

    All of these dashboards are designed with similar layouts but different visuals and metrics.

    Tip #3: Choose the Right Visuals for the Job 

    Visuals can be misleading. For example, if you’re missing key data sets, then the visual representation of the information won’t be accurate. Another common problem is choosing the wrong type of visual to display the data.

    To prevent this problem, your dashboard database should have access to all of the data you currently have in storage. Your in-house IT team or a third-party dashboard database designer can help you connect your data warehouse or cloud storage source directly to the dashboard.

    You should also choose the best visual representation for each data set. There are four main types of visuals:

    • Comparisons that show the difference between two or more sets of data;
    • Relationships that show how two or more sets of data correlate;
    • Distributions that group data by commonalities or values; and
    • Compositions that break data down into new categories, so you can drill down to get a more detailed look.

    The best dashboard database designs feature a combination of these four types of visuals. By analyzing the data from multiple angles using a few different custom visuals, you’ll get a much fuller picture of what’s happening at your company.

    Tip #4: Tell a Story in the Design 

    What’s the best way to organize all of these visual elements on the dashboard? You should structure the information like a news story.

    This means placing all of the most important data at the very top of the dashboard. Even if the stakeholder or employee only glances at the information at the top, they’ll understand the key insights. Your supporting visuals then go under these essential visuals—they are more detailed and enrich your understanding of the data.

    For example, a well-structured dashboard database design includes:

    • Top visuals: Big picture insights (e.g. a heat map showing total mortgage amount by county);
    • Middle visuals: Trends that support these insights (e.g. a line and bar graph comparing total mortgage amounts to goals set earlier in the year); and
    • Bottom visuals: Individual performance KPIs or operational details that lead to the figures above (e.g. a chart showing individual loan officer performance).

    When you structure your dashboard database design in this way, you make it easier for people to connect with the data through compelling visual storytelling.

    Tip #5: Keep it Simple 

    It’s easy to get carried away when you design a new dashboard from scratch. Many organizations pack as much information into the dashboard as possible because they believe it will lead to better insights. However, the opposite is usually true. Too many visuals or data sets on the page will only confuse people and make it harder to see what truly matters.

    A clean, simple dashboard database design is most effective. Not only does this make your dashboard look more polished and professional, but it also is easier to use on a daily basis. Users know exactly where to input data or filter results. They also know where to find crucial information so they can take immediate action.

    Include no more than about five to ten visuals or widgets on a single dashboard. Your entire dashboard should also fit on a single screen without requiring users to scroll down. This allows them to see everything at once. It also makes it easy to adjust data filters and see how the visuals change in response.

    Tip #6: Make it Colorful 

    Cohesive color schemes serve two purposes in dashboard database design:

    1. They make it easier to quickly understand the data on display. 
    2. They lend the dashboard a more professional look.

    For example, if you color-code the data so that profits are shown in green and deficits are shown in red, stakeholders will instantly know whether the figures are overall positive or negative. You can also show different gradients of color to represent information, such as in heat maps.

    You also have the option to choose background colors that match your company’s logo or official color scheme. When presenting this information to stakeholders, potential investors, or others outside of your organization, this makes your dashboards and reports look more authoritative.

    How to Create the Best Dashboard Database Design 

    Dashboard database design is both a science and an art. You need to be familiar with how to link the dashboard to data sources, program interactive components, and create innovative visuals based on data science best practices—but you also need to weave a compelling story and make a beautiful, user-friendly dashboard.

    Combining all of these traits can be time-consuming and challenging, particularly if you don’t have a data science or graphic design background. By hiring a third-party dashboard database designer, you’ll save time and effort. Dashboard experts will:

    • Connect all of your data to the dashboard;
    • Build a dashboard that answers key business strategy questions;
    • Choose the most appropriate visuals for each data set; and
    • Customize the dashboard based on staff workflows and your business philosophy.

    Having a fully-customized dashboard built by data science experts lets you identify the key insights and trends that will help your business blossom.

    If you’re ready to build aesthetic and functional dashboards for your organization, contact Tek Leaders today. We create custom dashboard database designs from scratch that help organizations gain valuable insights. If you have more questions about our design process, you can reach us by email directly.

      Author: Devender (Dev) Aerrabolu

      Devender (Dev) Reddy Aerrabolu is the CEO of Tek Leaders. His goal is to help SMBs bring value from their data. Dev helped Tek Leaders grow from scratch into a $25 million enterprise by focusing on clients’ data needs.

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      Power BI Pro Limitations: What You Need to Know

      Millions of people use Microsoft’s Power BI to generate compelling visuals and analyze complex sets of data. One of the most successful versions of this software is Power BI Pro, a paid subscription that gives users the power to share reports and information with other Pro users.

      This version of the software has a few weaknesses. Power BI Pro limitations include higher operational costs, data size restrictions, and logistical issues. These problems can affect your bottom line and make report generation harder.

      Before you make the switch from a free Power BI license to Power BI Pro, you should consider all of these limitations and have a plan in place to counter them.

      Six Power BI Pro Limitations 

      The main reason why organizations buy Power BI Pro licenses is because they need to share reports across the enterprise and encourage staff to collaborate on projects. For $9.99 per user per month, Pro users get access to all of the same features as Power BI free users. In addition, they can:

      • Share pre-built dashboards;
      • Share reports with anyone in the organization; 
      • Share reports outside of the organization (only with other Pro users);
      • Work on new reports as a team remotely; and 
      • Connect to Excel spreadsheets and other Microsoft support services.

      Power BI Pro expands on the offerings available in the free version. However, in some cases, the free version or other Power BI Pro alternatives are a better option. That’s because there are a number of Power BI Pro limitations that can hinder some businesses.

      Six Power BI Pro limitations include:

      • Higher cost: Although a Pro license only costs $9.99 per user per month, this adds up over time. Having just one user with a Pro license could cost your company nearly $120 per year, and if you have multiple staff members, this expense increases exponentially.
      • Data limits: You can only store 1 GB of data per file or data set. You also get 10 GB of storage total per user. This is a serious problem for organizations that need to analyze multiple data sets or large sets of data. You’ll run out of space quickly.
      • Restricted sharing: While you can share dashboards and reports with other users, these users must either be part of your organization or have a valid Power BI Pro license.
      • No dedicated capacity: You have to share computational resources with other Power BI users. This isn’t an issue in some industries, but many businesses prefer to have their own dedicated resource to bolster security and improve computational efficiency.
      • Limited data refresh: Your data is only refreshed eight times per day. For some organizations this is sufficient, but it may be too risky for those that can’t afford to lose important updates or data.
      • No access to Power BI Report Server: You can’t build on-premise data storage or keep your data behind a firewall. This is a problem for organizations that want to control their own data security and manage their own servers.

      These Power BI Pro limitations may not affect your company. For example, some organizations don’t require any extensive data storage or on-premise infrastructure. For these companies, Power BI Pro is a great option. For others, the cons of this system outweigh the pros. To decide whether this version of Power BI is right for your organization, consider your business strategy and budget.

      How Do These Power BI Pro Limitations Affect Your Business? 

      Power BI Pro is meant for small-scale or medium-scale businesses that want to create stunning visuals and share them within the organization. This version of the software is less expensive than many other visualization tools on the market. If you only need to purchase licenses for a small number of users and you have a few extra hundred dollars available in your annual IT budget, then upgrading to a Power BI Pro license is fairly cost-effective. The benefits outweigh the extra cost.

      However, if you have a tight budget or a large staff, then Power BI Pro’s limitations will eventually catch up with you. Buying licenses for dozens of users isn’t an option for organizations that have razor-thin margins or modest IT budgets.

      Additionally, some organizations may not find the benefits of Power BI Pro that compelling. You must decide whether it’s worth approximately $120 per year per user to gain the ability to share and collaborate on reports. For some organizations, this is a fantastic investment that will help their staff work more efficiently. For others, collaboration isn’t as important, so the free version is adequate.

      Or, your organization may need more features than Power BI Pro can provide. This software can’t handle large data sets, firewalls, on-premise servers, or unlimited sharing between organizations. If you need any of these features, consider upgrading to a Power BI Premium license or hiring an IT services firm.

      Should You Use Power BI Pro? 

      You can overlook many of these Power BI Pro limitations if you just want visualization software that’s easy to use and can be shared among your staff.

      However, many modern organizations demand more from their visualization software. To stay competitive and agile, more businesses are realizing that there’s significant value in custom visualization software. An IT services firm can provide you with secure cloud storage at a much higher capacity compared with Power BI Pro. The firm will also create custom interactive dashboards to help you make better business decisions. You can share all of this information with whoever you choose. It’s even possible to create user authorizations that allow users to only have access to certain types of data at any given time.

      All of these features are just as simple to use as in Power BI Pro, so there’s no additional training needed. Moreover, they’re priced based on the features you want, so you’ll never pay for services you won’t use. With a custom system designed by an IT services firm, you’ll avoid these Power BI Pro limitations and draw more meaningful insights from your data.

      If you’re looking for a custom visualization tool that fosters collaboration and sharing, contact Tek Leaders today. Our suite of visualization tools include self-service reporting, intelligent dashboards, secure report sharing, and comprehensive BI portals. If you have more questions about Power BI Pro or any other popular visualization software tools, you can reach us by email directly.  

        Author: Devender (Dev) Aerrabolu

        Devender (Dev) Reddy Aerrabolu is the CEO of Tek Leaders. His goal is to help SMBs bring value from their data. Dev helped Tek Leaders grow from scratch into a $25 million enterprise by focusing on clients’ data needs.

        August 27, 2019

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        The Pros and Cons of Hiring an In-House Business Analyst

          An in-house business analyst isn’t just a luxury afforded to successful Fortune 500 companies. These knowledgeable experts can help even the smallest enterprises become more efficient and successful.

          But how do you know whether you need an analyst? There are pros and cons of hiring these experts full-time. We’ll explain some of the benefits and downsides of having a business analyst on staff and help you decide which business strategy is best for your company.

          What Does a Business Analyst Do? 

          Business analysts help enterprises incorporate new technologies into existing systems and ensure that everything operates smoothly. They are essentially specialized project managers who have a deep knowledge of technical systems and data analysis best practices. Some analysts manage projects themselves, whereas others oversee the project manager’s tasks and focus on the bigger picture.

          In-house business analysts are salaried employees who work on projects full-time. This differs from outsourced business analysts who work for third-party IT firms. If you hire a third-party analyst, you pay the firm a monthly or annual fee (not a salary) to use the analyst’s services.

          Whether you hire someone in-house or use a third-party service, good business analysts are important. They not only understand how technology works but can also decide whether that technology is an appropriate fit for your business. Additionally, hands-on analysts may handle operational details like infrastructure, IT staffing needs, and internal communications. It’s a complex job that only a limited supply of qualified people can do well.

          A business analyst’s job may include many roles and responsibilities, such as:

          • Assessing the efficacy of the company’s existing technology
          • Creating a detailed business strategy to drive the company forward 
          • Finding new technologies or systems that improve efficiency 
          • Using data analysis and models to predict how new systems will impact the company 
          • Setting clear operational goals
          • Overseeing staff training
          • Hiring new IT staff or negotiating contracts with IT staffing firms 
          • Deciding which software and hardware to purchase 
          • Integrating new technologies 
          • Documenting and tracking changes 
          • Communicating with stakeholders and upper management 
          • Constantly reassessing systems and making improvements

          This is why many enterprises find business analysts so valuable. They take ownership of complicated technical projects and lead teams to success. However, deciding between an in-house or third-party business analyst can be a challenge. There are pros and cons of having an in-house analyst on staff. This guide will help you decide whether you need a full-time business analyst on your team.

          The Pros of Hiring an In-House Business Analyst 

          In-house business analysts can add a ton of value to a company. Some of the most notable benefits of hiring an in-house business analyst are:

          • Lower operational costs. The main reason why enterprises hire in-house business analysts is because they can save the company money. Part of their job is to find places to cut costs and maintain a lean budget. They also reduce costs by ensuring that projects are done correctly the first time, so you won’t waste money on repairs, last-minute changes, or relaunches. Additionally, analysts are experienced with managing IT departments, so they keep your IT staff on task. You’ll get higher-quality work out of your salaried employees for the same cost.
          • Faster and higher returns on investment (ROI). Your profit margins will likely increase with help from a business analyst. They accomplish this by improving operational efficiency and speed, only choosing to work on projects that they know will be profitable, and finding the most cost-effective tools to complete the project. You’ll see results faster. 
          • Greater employee satisfaction. Unorganized office spaces are stressful environments. Without a clear leader, IT departments can feel isolated from the rest of the company. It’s hard to stay on task and feel motivated when workers aren’t sure what they’re supposed to be doing or why their work matters. Business analysts solve this problem by stepping in as leaders and holding workers accountable. Effective business analysts communicate why certain tasks are important and help employees feel part of the process. 
          • Improved communication between IT and upper management. Many managers don’t understand how technology works and, as a result, they’ll accidentally cut important IT resources from the budget. This puts strain on the relationship between IT and management. The IT department won’t have the tools they need to do their jobs and management will feel frustrated that the IT department isn’t working fast enough. Business analysts speak the IT department’s language, so they’re less likely to make this mistake. They understand how the technology works and which tools are essential to keep. Analysts are also savvy business leaders. They can talk about sales funnels and ROI with stakeholders just as effectively as they talk about cloud computing and predictive modeling with IT staff. They make sure everyone’s on the same page.
          • Easier scalability. With help from a business analyst, you can pivot to a new strategy as quickly as possible. Great analysts won’t hesitate to change direction if they think it will lead to greater profits or efficiency. Your business will evolve with the times and stay on top of important tech trends.

          Almost all of these benefits also apply to third-party business analysts. The only difference is that third-party analysts usually don’t perform their tasks onsite. They manage teams remotely or assign tasks to their own IT experts. It comes down to personal preference. Some business owners prefer to work with analysts onsite throughout the work day, whereas others simply want to see results. Both types of analysts provide the same level of care and attention to detail. 

          The Cons of Hiring an In-House Business Analyst 

          Hiring an in-house business analyst isn’t necessarily the best choice for every company. Not only do some analysts lack the experience and leadership skills required, but a full-time analyst may not be in the budget for smaller enterprises.

          Hiring an in-house business analyst isn’t as beneficial if:

          • Your company doesn’t have any IT infrastructure. An in-house analyst can help you create an IT department from scratch or outsource your IT staff, but this is difficult. It may take longer to complete projects.
          • The analyst doesn’t have a clear strategy. Before you hire an in-house analyst, make sure that they have a detailed methodology or plan. They should have qualification measures, training tools or templates, quantifiable ways of tracking results, and defined goals for everyone they will supervise. You should never have to supply these systems or tools for the analyst—it’s their responsibility to handle these details. 
          • You have a very limited budget. A skilled business analyst may expect a competitive salary and benefits package. Top analysts earn $80,000 per year and up. However, the average salary is about $59,000 per year. This is well worth the investment for most companies, as the analyst can save you more money than you’ll spend on their salary. Still, this is a steep price for some smaller enterprises, especially those with very few people on staff full-time. 
          • You change direction infrequently. Some industries are slower to change than others. While all businesses benefit from routine business analysis and business intelligence (BI) audits, you may only have to do this once every year or so. Hiring a full-time in-house analyst may not be the best choice in this situation because the analyst won’t have enough to do. In this case, it’s wiser to hire a third-party consultant for one-off projects or occasional audits as needed.

          Not every company needs a full-time business analyst on staff. The good news is that you have a lot of options, even if you choose not to hire a salaried analyst. IT consultant firms that specialize in business analysis and BI strategies can help you achieve the same results as a full-time analyst, often at a lower cost.

          Do You Need an In-House Business Analyst? 

          Whether to hire an in-house business analyst depends on a number of factors, including your industry, budget, short-term goals, and long-term goals. In general, if you have a large in-house IT department and want to manage multiple projects at the same time, then an in-house business analyst can be very beneficial. The analyst will help you get your IT department organized and keep all of your projects on track. The analyst can also work closely with a third-party IT firm to complete projects quickly and efficiently.

          However, if you prefer to focus on just one project at a time, or you don’t have a large IT infrastructure in place, outsourcing your business analysis is a better option. You can hire a third-party business analyst that provides managed or professional services. Some analysts offer their professional services for one-off projects that you pay for per project or by the hour. Another option is to hire a third-party IT firm that has business analysts on staff. The firm’s business analysts communicate with you and direct their IT teams to complete tasks. You get all of the benefits of a large IT department without having to manage it or hire full-time employees.

          Regardless of which option you choose, thorough business analysis is essential for every company. When you assess your technology strategy and find ways to make your system more efficient, you’ll reap some fantastic rewards. 

          To learn more about the business analysis services we provide, contact Tek Leaders today. We offer multiple options, including business strategy consultations, full business intelligence audits, and managed IT services. If you have more questions about our team’s expertise, you can reach us by email directly.

            Author: Devender (Dev) Aerrabolu

            Devender (Dev) Reddy Aerrabolu is the CEO of Tek Leaders. His goal is to help SMBs bring value from their data. Dev helped Tek Leaders grow from scratch into a $25 million enterprise by focusing on clients’ data needs.

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            How Small Businesses Benefit from Data Analytics and Business Intelligence

                When you run a small business, sometimes it feels like you’re David battling Goliath. Industries like banking, healthcare, and insurance are especially competitive, making it difficult for smaller companies to gain a foothold. However, there is one thing that small business owners can do to increase their share of the market: Invest in data analytics and business intelligence.

                Business intelligence and data analytics help small businesses streamline daily operations, run leanly, and provide better products to their customers. Focusing on these two strategies will improve the services you offer locally and give your business room to grow.

                Data Analytics and Business Intelligence: What’s the Difference?

                To understand how data analytics and business intelligence (BI) can benefit your business, it’s important to know exactly what these systems do.  

                Data Analytics is the process of analyzing big data to identify patterns and make data-driven decisions. However, data analytics is only a very small piece of the puzzle. If you want your small business to run as efficiently as possible, then you also need a comprehensive business intelligence plan.

                Business intelligence (or BI) is a broad system of data tracking tools that help you make the most of your data analysis. This system includes technologies like data visualization, reporting, and user-friendly platforms that let you interact with the data in innovative ways.

                So data analytics is just one part of a larger BI strategy. These systems work in tandem to help your business succeed. Without a BI roadmap, it would be difficult to organize the data and make use of it. You’d generate insights but wouldn’t know which actions to take in response. Likewise, without data analytics, a BI system is incomplete. You’d have to guess which decisions are right for your business rather than basing these decisions on reliable data.

                By improving both your data analytics and BI strategies, you’ll maximize your small business resources and set your enterprise up for future growth.

                How Data Analytics Benefits Small Businesses

                There are many subcategories of data analytics that small businesses will find useful, including:

                • Consumer analytics: You can collect and analyze information about your customers to improve the services you offer and foster greater customer loyalty. Having a complete picture of your customer’s behavior and needs makes it easy to see what steps will earn trust and draw interest in your product. Data preparation and blending is a key part of this strategy, as it helps you organize all of this information and find hidden patterns.
                • Spatial analytics: Make your business run more smoothly by improving your office layout and organization. Spatial analysis deals specifically with how people behave in a given area. You can collect data about how your customers move through your store and place certain products at prime locations with great foot traffic. You could also analyze how well your staff navigates the office and make adjustments based on their preferred workflows. An organized office is a happy, productive office.
                • Fraud preventive analytics: You can analyze past fraudulent incidents and identify vulnerabilities in your system. Small businesses in particular cannot afford to lose any money to fraud, as it has a major impact on financial results. You can stop many of these incidents before they happen using advanced preventative analytics technologies, including machine learning.
                • Predictive analytics: Use data to pivot in new business directions and improve efficiency. In the past, predictive modeling was expensive and time-consuming. It was beyond the budget and expertise of small businesses. Today, you have access to state-of-the-art predictive models and tools at a low cost. Moreover, you can hire an IT firm to perform predictive analytics for you, saving you time and effort.

                Data analytics also improves small business logistics. With the right analytics tools, you will:

                • Save money: If you hire an analytics firm, you’ll have access to expensive data analytics tools at a fraction of the cost. You can also use data analytics to find areas where you can safely cut spending without compromising the quality of your products or customer satisfaction.
                • Save time: You won’t have to crunch numbers manually or keep long spreadsheets up to date. Many of today’s best data analytics tools are fully automated and intuitive to use.
                • Stretch limited space and resources: There’s no need to hire an in-house IT staff to perform data analysis. Offshore data analytics offers you the same results and don’t take up any space in your office.
                • Scale up or down quickly: Using data analysis, you can decide what your next business move should be. This allows you to stay ahead of market trends. With cloud-based data analytics tools, you can change locations without losing any important data. It’s a portable solution that can expand or contract according to your needs.

                The Benefits of Business Intelligence for Small Enterprises

                Many of the benefits of data analytics for small businesses also apply to business intelligence. However, there are also a few benefits that are unique to BI that will help your business really succeed. Those advantages are:

                • Identifying hidden inefficiencies: When you create a new BI system from scratch, you have to perform a full business audit. This will help you find problem areas that you may have missed over the years. Every business benefits from an occasional operational audit, but small business will notice the biggest difference after the audit is complete. Even the smallest changes to your system will have a ripple effect throughout the entire company.
                • Generating better data visualizations: Collecting and processing data isn’t useful if only a few people in your company understand what the data means. Visualizations encourage your staff to interact with the data. They don’t just see a series of random numbers–they see what these numbers actually mean.
                • Creating a better data culture in the workplace: The problem many small businesses face is that their staff feel ambivalent about data analytics. Why should a sales representative care about predictive modeling? Apathy leads to poor data governance, as your staff won’t care enough to upload data properly. When you make a BI user portal, it’s easier for your staff to manage data. They become part of the process because they know how to use these tools themselves. It makes data empowering, not confusing.
                • Selecting the right analytics tools: An effective BI strategy suggests the best tools to use for your business’ specific needs. You won’t pay for services you don’t use.

                How to Maximize Data Analytics and BI

                Small businesses don’t have access to the same resources as Fortune 500 companies. While a huge conglomerate can hire dozens of analytics experts to work on-premise full time, a small business usually doesn’t have this luxury.

                However, you can get all of the same benefits of data analytics for your small business when you outsource the process. A data analytics firm will walk you through every step of the process, from performing a BI audit and coming up with an effective BI strategy to teaching your staff how to use data visualization tools and process data. 

                If you’re ready to make the most of data analytics and BI, contact Tek Leaders today. We create custom BI strategies and data analytics tools for our customers based on their unique needs. If you have more questions about the benefits of BI and data analytics for small businesses, you can reach us by email directly.

                  Author: Devender (Dev) Aerrabolu

                  Devender (Dev) Reddy Aerrabolu is the CEO of Tek Leaders. His goal is to help SMBs bring value from their data. Dev helped Tek Leaders grow from scratch into a $25 million enterprise by focusing on clients’ data needs.

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                  Your Team Can Make Better Data-Driven Decisions. Here’s How

                    The best decisions are data-driven. In the past, business owners had to rely on instinct and guesswork when creating their business strategies. Now, they have access to state-of-the-art data analytics tools that are capable of making reliable predictions about every single decision and corresponding outcome.

                    How do you get your team on board with this new technology? You and your team can start making better data-driven decisions by following four simple steps.

                    Are You Making the Best Data-Driven Decisions?

                    Most businesses already use some form of data analytics. However, not all businesses are making the most of this data. If the analytics tools you use are too basic, you may miss out on a number of important insights that will help your business succeed. Likewise, if your team doesn’t know how to collect, structure, store, retrieve or visualize the data effectively, then you’ll be more likely to make poor business decisions.

                    To make the best data-driven decisions, you need more than just a few sets of data and a spreadsheet. You need to:

                    • Collect high-quality data using the latest data engineering best practices;
                    • Organize and normalize the data;
                    • Analyze the data and identify patterns; and
                    • Build a portal to share information with your team and allow them to collaborate on solutions.

                    This is a complex and time-consuming process with a steep learning curve. That doesn’t mean it’s out of reach, however. When you work with a data analytics firm that specializes in generating meaningful data-driven decisions, you’ll help your team make the transition smoothly. Here’s how.

                    Step 1: Collect Quality Data

                    To make better data-driven decisions, take a look at your data engineering process. Data engineering is a system of collecting and preparing data for later analysis. In other words, your team needs to collect the right type of data, verify that it’s high in quality and configure it so that it’s easy to access and analyze.

                    This is often easier said than done. If your team is used to collecting only a few types of data, they may not know how to properly incorporate and blend new data sets into the system—or how to tell the difference between high-quality data and flawed data. Moreover, these problems usually go unnoticed. Your team likely believes that they are collecting the right data already.

                    To determine whether you’re maximizing the data collection process, consult an IT firm that specializes in advanced data analytics. These firms have experienced data engineers who can help you refine your system architecture. They’ll also offer you suggestions on which types of data you should (or could) collect and explain the benefits of adding these new data sets to your system. 

                    Step 2: Organize the Data

                    Once you’ve improved your data collection methods, you’ll need to organize your data. This step will help you make faster and more accurate data-driven decisions.

                    One of the biggest mistakes that business owners make is jumping immediately from data collection to analytics. Unless you normalize the data first and ensure that it’s stored and organized properly, you’ll risk making inaccurate predictions. That’s because it’s difficult to blend and compare data sets that are stored in different formats or at different locations. For example, a key data set could completely change the outcome of your exploratory data analysis, but if your team doesn’t know where this data is stored, then it’s essentially worthless.

                    To normalize and store your data properly, you should work with a team of experts to revamp your entire system. You must use a consistent data structure in every aspect of your business. A data firm can help you move all of your data to a secure location (like a data warehouse), format it, organize it and create backups as necessary. Your team won’t have to waste any time on this process.

                    Step 3: Analyze the Data

                    Data collection and organization are just the foundations of good data-driven decisions. If you want to generate truly novel insights, then you’ll also have to expand your data analytics process. 

                    You should never take shortcuts with data analysis. It may be easier to input your data into a spreadsheet and generate basic visualizations, like pie charts or scatter plots, but that will only give you a shallow overview of the data.  

                    Your business deserves better. With help from a data analytics firm, you can generate much more detailed reports and blend separate data sets together to identify hidden patterns. You may even use predictive modeling and artificial intelligence (AI) to see how your decisions will impact your business in the future.

                    These advanced tools are complicated and take a great deal of time to learn how to use properly. Software licenses can also be expensive. By outsourcing the analysis process to a firm that already has access to these tools and years of experience using them, you’ll get all of these benefits with very little effort. This frees your in-house team to focus on implementing your decisions.

                    Step 4: Share the Data through a User-Friendly Portal

                    Usability can make or break your data-driven decision process. Even if you collect the best data, organize it perfectly, and use the most advanced data analytics tools, it won’t mean much if your team doesn’t know how to use the system. If they fail to upload data properly or aren’t sure how to generate reports, you won’t have the information you need to make the best decisions.

                    A data analytics firm can help. Not only will they train your staff on how to use the new system, but they will also provide a user-friendly portal where your team can perform all of their tasks quickly and easily. You can customize the platform according to your team’s workflow. Data is collected and uploaded automatically and you can compare multiple datasets at a time. Reports are also easy to find and create.

                    The system is designed with users in mind. Even team members with the least data experience can use the portal to perform very advanced analytics. It lets everyone make data-driven decisions.

                    How to Make the Best Data-Driven Decisions

                    While you can perform these four steps on your own, this process is time-consuming, expensive, and extremely complicated. It requires you to hire experienced in-house IT staff and build your own data storage systems and report generation platforms.

                    Hiring an IT firm to handle this process for you is much more cost-effective. You won’t have to build your own infrastructure from scratch or maintain any equipment. You’ll also speed up the decision-making process. Within just a few weeks or months, you can dramatically improve your data analytics process and start making data-driven decisions that will propel your business forward.

                    If you want your team to make better data-driven decisions, contact Tek Leaders. We provide every tool you need to collect, structure, store, retrieve and visualize your data. Our user-friendly portals make the process as simple as possible, no matter how much experience you have. If you want to learn more about the services we offer, you can reach us by email directly.

                    Author: Devender (Dev) Aerrabolu

                    Devender (Dev) Reddy Aerrabolu is the CEO of Tek Leaders. His goal is to help SMBs bring value from their data. Dev helped Tek Leaders grow from scratch into a $25 million enterprise by focusing on clients’ data needs.

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                    Using Data Analytics for Insurance Fraud Prevention

                    About $80 billion in fraudulent insurance claims are made every year in the United States, and this is just a conservative estimate. Many more cases of fraud go undetected because insurance companies don’t have reliable tools to identify them.

                    However, there’s a solution to this widespread problem. Using comprehensive data analysis for insurance fraud prevention, insurance providers can stop fraudsters in their tracks. This guide will show you how to use the latest data analytics tools to protect your staff, customers, and bottom line.

                    Insurance Fraud Prevention is Vital

                    The importance of insurance fraud prevention can’t be overstated. Even if just a few fraudulent claims go undetected every year, it can have a ripple effect on the entire company.

                    Below is an example of the impact that fraudulent claims have on your company, your customers, and the insurance industry as a whole:

                    • You lose money on the initial claim. Even if the payout is just a few hundred dollars, this adds up over time when other fraudsters use the same scheme in the future.
                    • Your staff wastes time processing fraudulent claims. That time would be better spent helping honest customers with legitimate claims.
                    • Your customers pay more for their premiums. To offset the added costs of paying for fraudulent claims, you’ll have to charge all of your customers higher premiums. The FBI estimates that US families pay from $400-$700 more per year due to insurance fraud.
                    • Your customers may have fewer options. If fraudsters take advantage of certain services, you might be forced to drop these services entirely in order to mitigate future risk. Your honest customers suffer due to the actions of a few dishonest policyholders or agents.
                    • Fraudsters will continue to take advantage of the loophole or vulnerability. If you don’t implement an effective insurance fraud prevention system, your problems will only compound over time. You’ll be seen as an easy target for people looking to game the system.

                    The only way to stop this ripple effect is to create a more effective insurance fraud prevention system based on the latest data analytics technology. Data analysis allows you to identify more fraudulent claims than ever before. In some cases, it may even enable you to prevent fraud before it happens. Here’s how:

                    Protect Your Company and Customers with Data Analysis

                    Which data analytics tools are most effective for insurance fraud prevention? It depends on the line of insurance that you offer and which business intelligence strategies you already use. Every insurance company has slightly different needs. However, most insurance companies in all lines of insurance should follow the steps below to improve their insurance fraud prevention strategies:

                    Step 1: Govern your data. Collect quality data from a variety of reliable sources and organize it effectively. You should obtain as much data as possible. This may include demographic databases, past insurance claims, user data (social media accounts, cell phone records, and ATM use), the policyholder’s financial records, and data from nonconventional sources. Data analytics experts can help you identify which are most important for your industry, store it effectively, and normalize it.

                    Step 2: Set up a descriptive analytics model. A descriptive analytics model shows you basic trends and patterns in your data, including cause and effect. This is the first layer of fraud prevention—it helps you identify when and why past incidents of fraud occurred.

                    Step 3: Set up a predictive analytics model. A predictive analytics model takes what you’ve learned from your past data and makes accurate projections about the future. The best predictive models use machine learning algorithms to improve the accuracy of these predictions. These algorithms constantly evolve and learn based on each new data set or pattern. You’ll get a specific rating for every new customer you sign or agent you hire, which determines how likely they are to commit fraud. This helps you identify potential vulnerabilities in the system before any fraudulent claims are filed.

                    Step 4: Set up a prescriptive analytics model. A prescriptive analytics model takes everything you learned from the other two models and helps you plot a course of action. This model focuses on the business side of insurance fraud prevention. Now that you know why past incidents of fraud occurred and who is likely to commit fraud in the future, you can structure your business to make it much harder to file a fraudulent claim in the first place.

                    Step 5: Revisit your insurance fraud prevention strategy frequently. Fraud prevention is a moving target. When a loophole closes, fraudsters look for others. Using the data analytics models above, you can identify some of these early cases of fraud before they become widespread.

                    However, one of the challenges of using data analysis for insurance fraud prevention is that your organization may not have access to all of the most advanced analytics tools. Your staff may also lack the training necessary to use those tools. A data analytics firm can take care of these details for you, so you can start using all latest insurance fraud prevention innovations without delay.

                    Which Data Analytics System Offers the Best Protection?

                    There are many different insurance fraud prevention analytics tools available on the market. Choosing the best software and hardware for your needs is time-consuming, and requires an advanced level of data analytics expertise. This is why many insurance companies choose to work with an experienced data analytics firm.

                    Knowledgeable firms handle everything from software licensing to data visualizations and user-friendly data portals. You won’t have to create a machine learning algorithm from scratch or train your IT staff on how to use a new, complicated analytics software. You’ll just see results.

                    This is especially important if you want to maximize the resources you have. A few years ago, a top 20 life insurance & annuity carrier wanted to improve its insurance fraud prevention strategy. The company was using an inefficient analytics tool for the job. They used spreadsheets to visualize patterns in the data, which was time-consuming and sometimes inaccurate. Moreover, their data sources weren’t normalized, so they couldn’t make very accurate predictions.

                    To solve this problem, the insurance carrier hired Tek Leaders to revamp its fraud prevention strategy. We provided the company with a platform that reports, shares, and analyzes data all from one place. We also normalized the data, which made the reports and predictions more accurate. Our changes led to a 30 percent reduction in cost and a 70 percent reduction in annual maintenance for the company. Most importantly, with our help, the company’s fraud detection capabilities were three times more efficient. They were able to identify many more potential cases of fraud.

                    By opting for an all-inclusive insurance fraud prevention strategy provided by an experienced analytics firm, you’ll offer your agents, customers, and shareholders the best possible protection.

                    Insurance fraud prevention isn’t a one-size-fits-all solution—every insurance company has different needs. To find the perfect fraud prevention system for your company, contact Tek Leaders today. We offer advanced fraud preventative analytics tailored specifically for our clients. Or, if you have more questions about our fraud prevention technology, you can reach us by email directly.

                    Author: Devender (Dev) Aerrabolu

                    Devender (Dev) Reddy Aerrabolu is the CEO of Tek Leaders. His goal is to help SMBs bring value from their data. Dev helped Tek Leaders grow from scratch into a $25 million enterprise by focusing on clients’ data needs.

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                    Transforming Your Next Quarter with Real Time Analytics

                    Next quarter is always on the way. So it’s a good time to ask questions and examine your data. Are sales or product performance in a slump? You may be looking for business strategies to pick up next quarter’s pace. Or maybe things are going well and you want to maintain steady growth. Either way, you can use real time analytics to gather data that can help transform your next quarter.

                    Real time analytics are powerful tools that not only gather live data but turn it into useful information. They offer a picture of what’s happening across your business or customer base so you can react to change or proactively create progress. Next quarter, draw upon a history of insights to help you plan your strategy.

                    Real time analytics foster a more data-driven culture that can help make next quarter more successful. Here’s how they can help you plan for your company’s future and make business goals a reality.

                    Real Time Analytics Unlock a Host of Opportunities

                    Real time analytics draws the information you need from your data. It starts the moment data is acquired, creating intelligence that’s nearly immediate. That helps minimize risk and downtime, while speeding decisioning and reporting.

                    Real time analytics unlock opportunities and supports a variety of technologies and industries, including:

                    • Retail—Leverage live information about customers to raise customer satisfaction levels
                    • IT—Analytics are used to enhance hardware and lower latency in memory chip architecture.
                    • Applied Science—Predictions and models based on complex data with many variables, such as in hurricane forecasting.

                    Your organization will have unique uses for real time analytics.

                    How to Transform Next Quarter and Beyond

                    Real time analytics can align your organization’s people, processes and technology through business intelligence. Your organization can outline a targeted goal, devise a strategy for getting there, and unite everyone to achieve it. Analytics-generated intelligence can help guide you along the way.

                    When using real time analytics to transform your company’s future, enact a governance process with well-defined accountability. This will help you to prioritize and manage data investments. You can refine your application and process architectures to adapt as needed and conditions change. There are self-serve analytics tools and powerful structured analytics solutions to help you do it all.

                    If you’re having trouble getting your system to generate the intelligence you need or are unsure of how to action those insights, ask an analytics expert for help. With many insights comes a lot of value—but only if you know how to apply them.

                    If you find future-quarter planning hard, it may be time to start using real time analytics. Drawing conclusions from data gathered in the present and in the past will give you a good idea of what the future will look like.

                    Are you ready to minimize risk, make decisions faster and avoid downtime through real time analytics? Then contact Tek Leaders today. Our firm specializes in data visualization and reporting capabilities, helping make your data more actionable. You can also reach us by email directly.

                    Author: Devender (Dev) Aerrabolu

                    Devender (Dev) Reddy Aerrabolu is the CEO of Tek Leaders. His goal is to help SMBs bring value from their data. Dev helped Tek Leaders grow from scratch into a $25 million enterprise by focusing on clients’ data needs.

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                    The Most Effective Data Analytics Strategies in the Insurance Industry

                    When I began my career as a financial reporter for the insurance industry, the data analytics process was incredibly time-consuming and messy. We kept all of our data in an information silo and if we wanted to analyze it, we had to do so by hand. What’s worse, we didn’t always have the most recent information at our fingertips. Brokers often had one set of numbers while underwriters had a completely different set.

                    To get more organized, my company hired an IT firm to handle the entire data analysis process. The firm used a number of proven strategies to improve how we collected and processed our data, including predictive modelingfraud detection, and demographic analysis. These three strategies can help you get the most value from data analytics in the insurance industry, and offer the best service to your customers.

                    The Importance of Data Analytics in the Insurance Industry

                    Data analysis is especially important in the insurance industry. That’s because agents, brokers, financial reporters and other insurance professionals have to gather large amounts of data from their policyholders and process every single number effectively. Everything from premiums to risk factors and a customer’s financial standing must be carefully collected and analyzed.

                    To get the most benefit from all of their data, insurance companies need to implement the most effective data analytics strategies. Those strategies are:

                    1. Use predictive analysis to optimize premium pricing options;
                    2. Collect data that can be used to detect and prevent insurance fraud; and
                    3. Analyze which lines of insurance are most useful to customers.

                    When you focus on these three methods, you’ll leverage your customers’ data in a meaningful way, make a greater profit, and offer the best possible services.

                    Use Predictive Analysis to Optimize Premium Pricing

                    Insurance companies use advanced statistics and predictive data analysis—commonly called the law of large numbers—to determine how much, on average, policyholders should pay based on their histories. However, this method isn’t foolproof. If an insurance broker doesn’t use the most reliable data analytics strategy, then the premium the broker calculates may not be accurate.

                    For example, a policyholder with a squeaky-clean record or who is in perfect health may be offered a premium that is much higher than it should be. That policyholder may well be dissatisfied with the service. Likewise, a policyholder with a spotty record or hidden risk factors may be offered a premium that’s much lower than it should be. The result is greater financial risk for the company.

                    By using the most reliable data analytics in the insurance industry, you can overcome these challenges. Experienced IT firms can collect and analyze this data for you, allowing you to make better predictions about your policyholders’ behavior and risk factors. In the past, this level of in-depth data analysis would have taken brokers and financial reporters far too long to calculate themselves. With help from a fully-staffed IT firm, this level of detail is much easier to achieve, resulting in far more accurate predictions.

                    After you improve your predictive analysis process, you’ll be able to offer your lowest-risk customers the low premiums they desire, helping you nudge out the competition. Meanwhile, your highest-risk policyholders will pay a fair premium based on their histories, shielding your company from significant financial burden.

                    Detect and Prevent Fraud in Real Time

                    Fraudulent claims have always been a serious problem in the insurance industry. The reason why this is such a difficult problem to solve is because brokers, actuaries and investigators often lack the information necessary to detect fraud in real time—much less prevent it.

                    But what if I told you there is a way to not only respond more quickly to suspected cases of fraud, but also to prevent them entirely? Implementing more accurate data analytics in the insurance industry is one of the most effective ways to combat this issue and stop would-be frauds in their tracks.

                    There are two ways to use data analytics for fraud detection and prevention.

                    • Gathering all pertinent information: IT firms can collect data related to all of your claims and screen them for common fraudulent tactics. The more information you gather, the better chance investigators have to identify the fraudulent claim. Cloud data storage and powerful analytics software make it easier to identify fraudulent claims that would have gone undetected in the past.
                    • Predicting when fraud is likely: Modern data analytics software can do more than just identify past cases of fraud—it also makes it harder for people to commit fraud in the first place. The most common auto insurance scheme happens when a policyholder claims damage to a vehicle, but never repairs the damage. Knowing this, you can require policyholders to submit proof of repair before they’re compensated fully for the claim. Predictive analysis helps you identify patterns like this and close notorious loopholes in the policy’s terms.

                    As predictive data analysis in the insurance industry becomes more common and extensive, we’ll likely see far fewer cases of fraud in the future. This, in turn, will reduce your company’s financial risk while decreasing the cost of premiums for honest customers.

                    Analyze Which Lines of Insurance Are Most Needed

                    Data analysis in the insurance industry can also help brokers create desirable new plans for their customers. Because insurance companies rely so heavily on the law of large numbers, customers often end up paying for services that they’ll never use. For example, a man without children may have a policy that includes dental and medical care for dependents under the age of five. He will never use this service and may feel that he’s wasting money on it.

                    By carefully analyzing the most common needs of your policyholders based on detailed demographic information, you’ll avoid offering your customers more or fewer services than they’ll actually use. While it isn’t possible to offer a customized plan for every individual, when you improve your data analysis, you’ll get much closer to offering plans that meet almost all of your customers’ needs.

                    The result is that your customers will only pay for the services they are likely to use, which improves their satisfaction. Moreover, your brokers will have an easier time marketing these plans to customers, which could lead to more sales and higher customer retention. It’s a win-win situation for your company and your customers.

                    How to Apply Data Analytics Strategies in the Insurance Industry

                    Now that you know which strategies to focus on, you’ll need an experienced IT firm to help you achieve your goals. Tek Leaders offers comprehensive data analytics for the insurance industry, including detailed consumer analytics, fraud prevention techniques, and predictive modeling. We have worked very closely with some of the top insurance companies in the country to help them provide the best services to their customers and reduce financial risk. Using these three analytics strategies, you too can harness the power of your data.

                    To learn more about the benefits of data analytics in the insurance industry, contact Tek Leaders today. Our team of experts will help you create an effective business strategy centered around detailed data analysis. Or, if you have more questions about our data analytics services, you can reach us by email or call us at (214) 504-1600 directly.

                    Author: Alex S. | Financial Reporting Manager

                    Alex S. has an extensive background in financial reporting, specifically as it relates to life insurance. As a consultant for Tek Leaders, Alex helps find some of the best solutions for our clients.

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