How to Make a Custom Data Analytics Software (DAS)


Business intelligence and data analytics software solutions have become go-to tools for modern-day businesses. According to Forbes , last year alone saw 53% of companies using the power of big data. Typical uses of data analytics software include customer analysis and behavioral studies.

As is often the case, financial services and telecom providers were the early adopters of advanced analytics tools. However, how can your business make use of these resources? We’ve put together a helpful guide on navigating the ins and outs of data analytics.

Why You Should Learn How to Build Data Analytics Software

Looking at the market of BI (Business Intelligence) tools, one can’t help but notice a fair share of off-the-shelf subscription-based solutions. With such a broad choice, developing a custom tool for yourself seems unnecessary at first glance. However, based on the experience of our clients, we’ve learned that custom data analytics tools still do companies a lot of good.

Here are just a few reasons you might want to prioritize creating a new solution over buying one from a vendor:

  • You can upgrade the software as the company grows. For businesses that are constantly searching for new markets and developing new products, an off-the-shelf solution won’t be the best fit. Your business infrastructure will outgrow the tool in a matter of months. In this way, a custom data analytics tool makes more sense - developers upgrade and improve it as the company grows.
  • Your data is secure and isn’t processed by vendors. By using third-party software, you’ll have to involve another company in your businesses data. While in most cases, the use of off-the-shelf analytics software does not result in data breaches, there are still some risks when it comes to storing sensitive personal data (addresses, credit card info, names, etc).
  • Self-service analytics. One of the benefits of data analytics software made from scratch is from providing self-service analytics. You can generate custom reports to visualize data in the most comprehensive way. It’s no secret that every business has access to thousands of gigabytes of data. By prioritizing the data, visualizing and presenting it properly you can fully benefit from the information you've collected.

What problems does data analytics software solve?

Given the exploratory nature of data science, sometimes the insights can bring about unexpected value out of the blue. That’s not to say, however, that all the benefits of data analytics software are hidden. There are clear purposes the software serves as well. For example, analytics software:

  • Displays data in a compelling way to make management, pitching, etc. easier. Visualized and presented well, the data can be a lot of help. In the form of responsive charts, tables, and graphs, the information can come in handy during a team meeting, investor pitch, or strategizing.
  • Prioritizes customer data by its importance. These days, companies have large amounts of data on their customers - startups can possess gigabytes of information on each user. Facebook, for one, may have up to 800 pages of data for each profile registered. But not all of this data is equally important. Sorting and prioritizing information is a major purpose of data analytics software. As a business owner, you’ll be able to know which customer info is crucial to improve your efficiency and which shouldn’t be as much of a priority at the moment.
  • Consolidates all the company’s data in one place. With dozens of dispersed logs, databases, and records, businesses end up having their customer data in different places. So it may get in harm’s way when trying to manage them or solving the security issues. Now, with the enforcement of GDPR, it’s especially important to take control over all the data your company has collected since its existence. With a data analytics tool, you’ll be able to put all the company’s data in the same place, organize it, and protect it efficiently.

Features a Good Data Analytics Software Development Product Has to Have

If you have decided in favor of custom data analytics software, the next crucial step is to determine what features the tool will have. While some of those depend on the niche, the business is working in or the type of operations the company conducts, there are a couple of tips for finding the essential features of a good data analytics tool. Without those, a solution won’t be able to perform its core functions.

  • Scalability. An efficient data analytics tool should be able to deliver results for massive datasets as well. The transition between small and large data sets is crucial. The less time and effort is spent on it, the better it is for the business.
  • Data filters. During the data-driven research, data scientists spend a lot of time organizing, cleaning, and systematizing sets. Analysis tools should be helpful when it comes to data management. Most common data analytics software features are range of data exploration features and APIs that provide seamless integration between various types of data and support for different data scenarios.
  • Data security. Another critical feature of every good analytics tool is data governance. It provides a data scientist with the ability to track sources of information. Now with GDPR, privacy is another concern to deal with. That’s why modern business intelligence solutions have advanced security features such as pseudonymized data.
  • Mobile access. A vast share of day-to-day business processes is dealt with via mobile devices. Hence, it’s crucial that a business owner, as well as data scientist, has access to the tool on the go.

These features are hard to come by when you get an off-the-shelf tool. If you’re seeking a potent data analytics tool, it’s better to contact a team of developers who can create it from scratch. At Trust Sourcing, we make sure to include all the most needed features to the BI tools we develop. When developing ImoXchange, we aligned the features of the tool with business requirements: generating reports in minutes, integrating payment system into the service, etc. Off-the-shelf solutions don’t have this kind of flexibility - that’s why you can only get powerful features through creating a custom software solution.

Development Process and Data Analytics Software Cost

Depending on the goals of the company, its size and the amount of data operated, the procedure of building BI software can either be more complex or, on the contrary, rather basic. Regardless of how complicated the software, here are the essential steps of building the best data analytics software.

Step #1. Defining your goals

Setting the objectives for a future product is the first and one of the most important steps taken in the entire process. To make sure that developers and business managers are on the same page regarding the software, they must sit down to discuss the most important points.

Step #2. Determine data parameters

As soon as you have laid out the main stages of your future business intelligence software, it’s time to discuss data analytics itself. This stage consists of two main things: choosing the data to measure and defining the parameters of measurement.

Step #3. Data gathering

Before you look for new data, make sure to collect everything you have at hand. You can find a lot of information from existing databases. The next step is collecting new data. There are a lot of available resources that come in handy (the most popular ones are Google Query and Kaggle).

When the data is collected, researchers organize it by creating a naming system and keeping operation logs.

Step #4. Decide which data to use

The next step to make a custom data analytics software is sorting through all the information you collected. In order to do this, developers create a system of data filters that will later be used in the software itself.

In order to analyze data for future BI software, you can use a few tools. Some good options include Stata, Visio, and Minitab.

Step #5. Create analytical queries and algorithms

In an article on Hackernoon, one of the top tech communities, Cathal Horan wrote, “The future is algorithms, not code”. For data science software development, writing algorithms is crucial. These algorithms ultimately help researchers build patterns by clustering data and classifying it.

AI (artificial intelligence) is on the verge of mass usage and accessibility, and a fair share of developers already use machine learning and predictive analytics to create algorithms for data analytics.

Step #6. Test the tool by analyzing different data sets

When the data analytics tool is created, it is still too early to launch it. Testing is essential to fix bugs and ensure a user experience works just the way the developers want.

Typically, it takes over a month to test a solution. The procedure is completed by analyzing the data sets of different sizes during various time frames. This way, developers ensure that a solution is multifunctional and, if need be, scalable.

Data analytics software cost

The cost of a BI solution can vary depending on the features you want to add, the level of customization you need, and the number of people that are going to use the tool.

  • When it comes to off-the-shelf solutions, it’s important to pay attention to the distribution model (on-premises vs. cloud), the license model (is the subscription charged by the number of users, data stored, etc), and the number of features. The overall cost of these solutions can vary from $400 to over $1,000 per subscription for one user.
  • Cost of data analytics software depends on the number of specialists involved on the team and the number of working hours they put into it. A cost of basic custom data analysis software tool developed by local tech talent will be different from that developed by a dedicated team. In order to cut the costs of software development, business owners prefer hiring a dedicated team over hiring an in-house department.

Let’s look at why working with an in-house team might no longer be a development solution.
Assuming that the project will take one month of everyone on the team working full-time, and considering the hourly rate of an American tech specialist according to PayScale, we can calculate the average cost of a data analytics tool.

The month of a full-time job consists of 21 work days. Considering one workday has 8 working hours, the total amount of hours is 168 per month.

  • System administrator - $35 per hour - $5 880
  • Database analyst - $37 per hour - $6 216
  • Data analyst - $33 per hour - $5 544
  • Project manager - $79 per hour - $13 272
  • QA tester - $36 per hour - $6 048

Assuming it would take only one specialist in these fields to complete your product and the month of work, you would still end up with a total of $36,960.

Business owners in need of custom BI tools are continually looking for ways to reduce the costs of development. One approach is outsourcing the engineering to a dedicated team from a third world country rather than hiring an in-house department.

Top Data Analytics Software Examples

1. OpenRefine

OpenRefine is one of the top Python-written tools for data parsing. The tool has all the features needed for SMB data analytics.

Main features:

  • Easy browsing thanks to interactive filters
  • Different input and output formats which allow integrations with Google Sheets and Fusion Tables
  • Record boundaries can be selected in one-click which makes creating data grids easier

Pricing: OpenRefine is free to download.

2. Talend

Talend is an open source data management tool. Developers cite an easy-to-use interface and customization as the main advantages of this solution.

Main features:

  • A huge set of tools and framework integrations for coding-free data visualization
  • A variety of connectors for SaaS, Enterprise, etc.
  • User community to share knowledge and experiences or seek out help
  • Changes can be tracked in real time

Pricing: $1,170 per month for one user;

3. NodeXL 

NodeXL is a tool to provide network insights for businesses. The tool is useful for monitoring the social network presence of your brand and those of your competitors.

Main features:

  • Advanced visualization with various options of algorithms, colors, and shapes for your graph
  • Supports a range of data importers, allowing you to collect data from all popular social networks (Facebook, Twitter, Youtube, etc.)
  • Automated data collection, visualization, and publication

Pricing: The basic version of NodeXL is free. NodeXL Pro costs $749 per year with a commercial license.

4. Gephi

Gephi is another open source data exploration and visualization solution with a robust set of features. It is compatible with all OS.

Main features:

  • Implement force-based algorithms
  • Community error detection system
  • Real-time visualization feature
  • Dynamic data filters
  • Create data cartography

Gephi is free to use.

5. Weka

Weka is one of the fundamental data science tools. While it can be used for business purposes, it is often used by data scientists and developers themselves thanks to the broad range of features.

Main features:

  • Reprocess data with 49 dedicated tools
  • A broad range of clustering algorithms
  • 10 search filters for data selection
  • 3 user interfaces

Pricing: You can download Weka for free.

To Sum Everything Up

Businesses in various industries have already started implementing BI tools to analyze and collect customer data. This type of software allows companies to collect insights, improve customer service quality, and make the buying process shorter and more efficient. Moreover, data analytics software can be used to improve operations within the business, including management, human relations, communication, accounting, etc.

While developing a custom data analytics tool, it’s important to get in touch with a team of professional developers and data scientists. At Trust Sourcing, we provide businesses with top-notch BI solutions to analyze, visualize, and utilize data in new, more efficient ways. We are happy to work on projects for SMEs and big companies alike.

Are you looking for a team to create a data analytics software? Send us a mess age and let's create better tech solutions together!

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