Statistical data analysis used to be a confusing, sluggish process. Calculations were all performed by hand. If even one key data sample was missing, it would ruin the accuracy of the entire report.
The best decisions are data-driven decisions. 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.
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 LINK TO DATA BLENDING POST WHEN LIVE on 5/21 [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.
Shashank Reddy Tummala.
If your business doesn’t use data blending, it isn’t running as efficiently as it could. Data blending helps nearly all modern businesses, from insurance companies and healthcare providers to manufacturers and financial institutions.
So why doesn’t every company already use it? One reason is that many business owners aren’t sure where to start. They need to know which data sets to merge, how to blend them, and how to generate insights from the data. It’s a complex process.
This guide will help you identify the right data blending strategy for your business. You’ll discover all of the benefits of this process and the best blending tools and methods. When you embrace data blending, you’ll generate powerful insights that will propel your business forward.
What is Data Blending and Why Does Your Business Need It?
Data blending is the process of merging data from multiple sources. It focuses on bringing all of your big data together in one place, allowing you to compare data sets and draw new insights. You can merge data that you collect in-house or compare your data to information collected from a third party.
Data blending is very different from data integration. When you integrate data, you normalize it or store it in one place (such as a data warehouse). This process makes your big data easier to find and use, but it doesn’t allow you to directly compare data sets or generate insights. To do this, you need a blending strategy as well as an integration strategy.
For example, let’s say that you wanted to compare these two data sets:
- Total sales by month at 10 branches in 10 different cities.
- Monthly sales made by every employee at every branch.
If you just put all of this information into a combined spreadsheet without blending it first, you won’t learn much from the data. You’ll also end up with duplicate data. When you filter the data by branch location, for instance, you’ll get all of the total branch sales figures as well as the individual employee sales figures. Redundant data is difficult to process and visualize.
When you run the two sets through blending software first, you’ll eliminate duplicate data and make fast, accurate comparisons. Data blending software is designed to look at data from multiple angles and filter results based on many dependent factors. For example, the software could generate:
- A chart showing who the highest and lowest performers are at each branch, on average;
- A projection of what next month’s sales figures will likely be based on employees’ past sales;
- Recommended quotas for each branch and employee to keep everyone on target; and
- Lists of employees who make the most sales. If most of these employees work at the same branch, you may find out what this branch is doing differently or transfer a few members of the team to struggling branches.
With the right data blending strategy and software, the possibilities are endless. Even a small amount of data can generate hundreds of new business insights and help you make better decisions.
Which Data Blending Method Should You Use?
There are many data blending software options available, so choosing the best one can be a daunting task. Businesses typically use the following tools to blend big data:
- Excel: You can join different spreadsheets, normalize data, and create formulas to automatically compare figures or organize data. Data blending is not the primary focus of the software, so most of this work must be done manually by someone who knows how to use these features.
- Tableau: This software focuses more heavily on the data blending process. You can use this software to blend data from multiple sources and generate insights or visualizations. However, one challenging aspect of this software is that it may require some training. For example, you may need to know when it’s appropriate to use a join (combining related data based on a common field) or when to use more advanced data blending tools.
With both of these options, there is often a steep learning curve. Tools like Excel and Tableau are capable of generating very powerful insights. However, using these tools effectively requires training and data analytics experience.
This is why many businesses seek help from business intelligence (BI) experts that already understand how to use these and other BI tools to maximize data’s utility and value. When you hire an IT firm to integrate and blend your data, you’ll make more accurate predictions about your business. You’ll also improve your efficiency, as you won’t have to train your IT department to use new tools.
Data Analytics Firms Can Help You Maximize Your Data
The main benefit of hiring a firm to handle your data blending needs is that experts can offer you a greater number of blending options that you may not have considered or don’t know how to generate yourself.
For example, if you have data in multiple spreadsheets that come from different data sources, filtering it is complicated and time-consuming. You may have to use multiple filters. Instead of that, experienced BI firms use specialized tools like dynamic parameters. Dynamic parameters make it easy to filter multiple sets of data using just one parameter. These calculations require some experience and skill to use properly.
Because BI firms have years of experience blending data and using state of the art BI software, they can help you do more with your data. They know the most effective methods for generating every type of prediction or visualization your business needs. Tek Leaders has dashboards for:
- Loan calculations by location
- Mortgage production reports
- Loan exception reports
- Loan activity
- Loan calculations by type
- Delinquent loan reports
- Commerical loan reports
These dashboards can be used for more than just complex loan calculations. We customize each visualization dashboard based on your business’ unique situation. You simply upload the data to the warehouse and select which type of report you’d like to generate. And if you want to start tracking new sets of data in the future, we’ll help you make this transition.
Data blending is a powerful tool. With help from the data analytics industry’s leading experts, you can take full advantage of this technology and make ever-more thoughtful choices for your business.
Data blending doesn’t have to be complicated. Contact Tek Leaders today if you’re ready to make the most out of your data. Our team of experts will build customized systems for extracting and comparing information to generate valuable insights. Or, if you have more questions about our data blending process, you can reach us by email directly.
Shashank Reddy Tummala.
FinTech disruption is changing how we think about banking. In the past, customers had to visit a branch just to deposit a check. Now, many deposits are made directly from phones, whenever and wherever needed.
This convenience comes at a cost. Traditional banks have to make major changes to their business strategies. They also have to consider security issues, infrastructure limitations and budget. In recent years, many banks have struggled to overcome these challenges.
But with the right business intelligence (BI) strategy, banks can regain their share of the financial market. FinTech disruption doesn’t have to be the nail in the coffin of traditional banking. It can be the driving force that encourages banks to provide better services to their customers and become more successful in the future.
How FinTech Disruption Affects Traditional Banks
FinTech is a broad term that includes any technology that companies use to manage finances or provide financial services. Chances are, you already use FinTech in some form. For example, if you’ve ever purchased something online (either by credit card or an online payment system), you’ve used FinTech.
This type of technology has been around for many decades. It’s been used to some degree since the 1860s, with the invention of the first transatlantic telegraph cable. Today, we’re seeing the greatest FinTech advances in online shopping, mobile banking, and cryptocurrency. Auto lenders are using fintech to stay competitive in a challenging market. As FinTech evolves, financial management becomes much more efficient and convenient.
However, while there are many benefits to FinTech, it also causes a number of problems, especially for traditional banks. FinTech disruption has made it harder for many financial institutions to attract and retain customers. It’s also made customers more vulnerable to data breaches, identify theft and fraud. Some of these new, online-only merchants have fewer security protocols compared with traditional financial institutions.
The FinTech disruption trend began when companies started offering end-to-end financial transactions online and in the cloud. Customers no longer have to purchase goods using a credit card or keep their money in traditional banks. They can now buy products and accept payments for services through an online merchant. It’s enough to make you use harsh words like “disintermediation”.
The competition is stiffer than ever. Because end-to-end services are so convenient, some customers are choosing online providers over traditional banks. Even though banks often offer better security and fraud prevention, some customers still choose convenience over safety.
Customers also expect more from their financial institutions. For example, these services were once rare, but are now standard:
- Online account management;
- Mobile check deposits;
- Online billing or person-to-person payments;
- Paperless statements;
- Live, 24/7 customer support (by phone and online chat); and
- Fast online loan and credit card approvals.
If banks hope to compete in the modern financial industry, they must take action now. The good news is that, with the right tools, traditional banks can overcome FinTech disruption and offer customers all of these essential services.
What Can Traditional Banks Do to Fight Back?
Traditional banks don’t have to become obsolete. Banks that leverage the power of business intelligence (BI) can compete on the same level as online providers. In many cases, they can offer better services because they have more resources and experience.
The challenge is to expand the online and mobile services without compromising security. This isn’t always easy; there are many factors to consider before this transition. With help from an experienced IT firm, you can incorporate business intelligence into your strategy while meeting every compliance standard.
An IT firm will first help you create a BI roadmap. Experts will assess your existing systems and provide you with a list of possible solutions to help your business run more efficiently. They may suggest:
- Building a new user-friendly customer portal for online and mobile banking;
- Building a portal for internal use that employees can access more easily;
- Creating algorithms to prevent fraud and flag suspicious account activity;
- Transitioning to cloud computing and storage;
- Encrypting private data;
- Keeping secure backups of important data;
- Improving the automatic loan and credit card approval process; and
- Providing customers better online banking support.
The main challenge that traditional banks face whenever they make significant changes to their infrastructure is ensuring security and regulatory compliance, since they’re responsible for highly-confidential data.
For this reason, many large traditional banks choose private cloud storage over public cloud storage. The main difference between the two is that a private cloud is hosted internally and often protected by a firewall, while a public cloud is hosted by a third party that uses its own security protocols. Although a private cloud appears more secure than a public cloud, they can, in fact, be equally secure. Trustworthy public cloud providers maintain very strict security and encryption standards.
The option you should choose depends on your infrastructure and resources. Larger banks typically use private cloud storage because they have the resources necessary to store and maintain their own servers and hire an IT team to keep them running smoothly.
Smaller banks may choose a public cloud server because it is much more cost effective and efficient. When you make the switch to the cloud, you’re able to provide more online services to your customers, such as account management, payment options, and fast approvals on lines of credit. All of your data is stored securely in one place, which allows you to generate important insights. You can also use the most advanced cloud-based fraud detection software, which will help you protect your customers.
However, not all financial institutions are able or willing to make the transition to the cloud. Even if cloud computing or storage isn’t right for your institution, an IT firm can still help you make your existing system run more efficiently. These firms can provide you with the best customer portals, data analytics and 24/7 IT support. Your customers will appreciate having access to convenient FinTech features and you won’t have to manage these services by yourself.
Traditional Banks Can Become FinTech Disruptors
By embracing BI and online banking tools, you’ll not only be able to compete in a post-FinTech disruption world, but you may even become a disruptor yourself. FinTech isn’t just for online merchants or cryptocurrency companies. Traditional banks can also use the latest FinTech advances to get ahead.
Improving your online presence, offering your customers better online support and leveraging the latest BI tools will not only save you time and resources, but it will also attract new customers. The line between FinTech disruptors and traditional banks is starting to blur. You can spearhead this new trend and become a leader in your industry.
If you’re ready to become a FinTech disruptor, contact Tek Leaders today. Our business intelligence experts will evaluate your business strategy and identify the tools you need to compete in the modern financial industry. Or, if you have more questions about our state of the art tools or services, you can reach us by email directly.
Shashank Reddy Tummala.
Big data facilitates big ideas. When you use big data and business intelligence (BI) in tandem, you can improve the quality of your data analysis and reach important growth milestones faster and more efficiently.
Statista surveyed 65 Fortune 1,000 companies that had recently implemented new big data and business intelligence strategies. Nearly 80 percent of them reported a dramatic increase in the accuracy of their data analysis. They also made better business decisions overall.
To get the most out of your own big data projects, you need to know how to effectively leverage your business intelligence systems. Here’s how.
The Relationship Between Big Data and Business Intelligence
Before you can use business intelligence to maximize your big data, you should consider how they relate to each other.
Big data is defined as any large dataset that companies use to find patterns or trends. This type of data is used most often to track customer behavior, determine the profitability of individual products or services, and predict business outcomes.
Business Intelligence (BI) is a system that analyzes big data and makes it actionable. This can be done manually or using specialized software. Every company has its own unique BI strategy.
You can’t make the most out of your big data without using business intelligence. However, not all BI systems are equally effective. For example, if you parse your data manually, you could easily miss several important insights and trends due to the complexity of the information you’re analyzing.
To squeeze as much valuable insight from your big data as possible, you should look into implementing an advanced BI strategy based on the latest analytics software and data storage techniques. Your BI strategy should:
- Improve your database and ensure that no data is lost;
- Allow you to perform more thorough data analysis using fewer resources; and
- Use predictive analysis and machine learning to generate insights.
By focusing on these three BI strategies, you’ll maximize your big data’s value.
Use BI to Improve Your Data Storage and Database Systems
To make the most out of your big data using business intelligence, start by making improvements to your database and cloud storage systems. This is especially important if you work with large data sets, as data can easily get lost if it isn’t organized properly.
There are three main challenges you’ll face when you gather and analyze big data:
- Volume. The amount of data you need to store dictates which BI systems you should use. For example, if you have to store terabytes of data, then you probably can’t use a storage system like magnetic tape or hard disks because the cost of storing all of this data would be exceptionally high. You’ll need to use cloud storage instead.
- Format. Many companies gather data from a range of sources and in a number of different formats. Your BI system should be able to process all of them and blend them into a single visualization.
- Speed. You should be able to upload and analyze data as quickly as possible. Some data, especially numbers used in real-time analytics, have to be processed very quickly in order to be useful.
To address these three challenges, the best modern BI systems use storage and organization tools like:
- Compression algorithms and formats to make storage more efficient;
- In-memory processing to reduce latency;
- Offshore servers that don’t take up space on the company’s campus;
- Columnar databases to more efficiently read data from hard disks (if they’re still used);
- Parallel processing to make data analysis faster;
- Data warehouse virtualization tools like Denodo that are easier to maintain; and
- Cloud storage and computing services that allow you to store a greater amount of data.
Even if you need to store a massive amount of data in a number of different formats and analyze it very quickly, these tools will help you do it. This is especially important in the insurance industry. Insurance companies work with some of the largest and most complex big data systems in the world, so they need very advanced storage systems to keep everything organized.
Analyze Your Data More Effectively with Business Intelligence
Another way that business intelligence helps you make the most out of your big data is by increasing the reliability of the insights you obtain from your data sets. An effective BI system improves:
- Consumer analytics. Many companies have an incomplete picture of their customers’ behavior. They either lack the resources to gather this data or they’re not gathering the right information. When you use an effective BI system and analytics software (like Alteryx), you can more clearly see what pieces of information you’re missing. This tool helps you prepare your big data and identify the areas where you can improve. It then offers insights into your customer’s behavior and what you can do to better meet their needs.
- Fraud prevention. As with customer analytics, many businesses aren’t gathering the right information needed to prevent fraud. Technology firms that specialize in data analysis and BI can help insurance companies and banking institutions identify fraudulent claims and use this information to prevent future cases of fraud. These BI systems can be tailor-made to fit the precise needs of the company.
- Spatial analysis. Retailers that operate brick-and-mortar stores need to gather big data about how customers behave in time and space. The problem with this data is that it’s complicated and time-consuming to gather. With tools like Alteryx, you can more easily collect this data and determine where you should position everything so that your customers visit more often or purchase more products.
These are just a few types of data analysis that business intelligence improves. Nearly every industry will see immense benefits from using business intelligence to gather and analyze big data.
Take Advantage of Predictive Modeling
Perhaps the most innovative and exciting way that business intelligence is improving big data is through advances in predictive modeling. Predictive analysis and modeling is the process of using big data to make detailed predictions about your business.
Industries have done this for centuries, but it has never been 100 percent reliable. Insufficient data, calculation errors, and other factors will make your predictions less accurate. However, modern BI is making these predictions much more reliable, especially through the use of machine learning.
For example, now that you can store greater amounts of data in the cloud, you have a larger data pool on which to base your predictions. When combined with machine learning—algorithms that learn from existing data sets and adjust their predictions accordingly without being programmed explicitly to do so—these predictions become even more reliable. They are often more reliable than predictions made by humans, because a machine can analyze multiple data sets and will make fewer errors in its calculations.
The only time that machine learning algorithms produce inaccurate predictions is when the big data itself is flawed. This means that as long as you collect accurate data and provide the algorithm with a sufficient amount of it, you will receive the most reliable prediction that’s currently possible.
Today’s predictive analysis and modeling techniques are more complex and sophisticated than ever. They can handle internal and external data sources, making your predictions much more accurate. They also make it easier for you to decide what to do with the new information you have. The algorithm will provide a single score for a given data set, allowing you to see exactly what needs to be done—there’s no guesswork involved.
How to Leverage Big Data and Business Intelligence
Big data and business intelligence go hand in hand. When you use the best versions of both, you set your business up for success. But how do you know whether you’re using the most effective BI strategy to achieve your goals? Creating your own BI strategy from scratch is a time-consuming and complicated process, especially if you have very little experience with data analytics and cloud computing.
This is why you should hire an IT firm that has experience creating custom BI systems. Reliable firms have an arsenal of analytical tools that they use to make the most out of your big data, no matter how complex it is. This also takes a great deal of work off of your shoulders. You and your in-house IT team won’t have to learn how to use and implement new data analytics systems or buy costly software licenses. You can focus on generating insights from your data and making your business the absolute best it can be
If you’re ready to make the most out of big data and business intelligence, contact Tek Leaders today. We provide customized data analytics and storage systems that allow you to visualize your data and make it actionable. If you have more questions about the big data and BI services we offer, you can reach us by email directly.
Shashank Reddy Tummala.