Self-Service Analytics – For Accelerated Adoption and Outcomes

Business Analytics – Forms

Business Analytics has been around from long time, in fact ever since the humans have done Business of some sort. The core Business Analytics lies in the Data Collected for the Business activities.

Stone Tablets with Data Collection

The most rudimentary form of Data Collection lied in the form of recording transactions and cash flows in books in the form of the times, e.g., markings on a stone tablet or paper books. The distinguishing feature that would mark these recordings towards Business Analytics is the evidence that these were utilized to make Business Decisions to drive Revenue growth or Profitability of some sort in the future.

While the simple constructs of Bar-Graphs and Pie-Charts are probably an invention of the recent times, relatively speaking, the concept of understanding Trends in data probably have been around for very long. A Line-Chart is therefore so very natural for human eyes to decipher in terms of Trend detection.

Business Analytics, as we know it today, may have gone beyond capturing the past and Trend detection via reports and charts. Yet, the basic constructs of it remain similar in most applications. Even in the most advanced Machine Learning methods to detect trends, the basic construct is to detect patterns or trends in data that signify distinction from the population characteristics or difference in trends from a notional baseline.

Advent of Computational Machinery

Distribution and Spread of Data (X’s)

The advent of the Advanced Computational Machinery that encapsulates the Mathematical constructs of capturing patterns and trends have made it possible for non-Mathematicians or Algorithm experts to build Models to Predict the future of their business in terms of Revenue Forecasts, Customer Acquisition or Retention Numbers, Product Success in the Market, and more.

The above trend is new as the skills required to build such Models were limited to Statisticians, Mathematicians, and Algorithm experts only a decade or so back. The main reason why this shift is seen is the Effort and Vision of Entrepreneurs in the space of Business Analytics pushing the envelope to demand such skills to be captured in Computational form and made available in the form of Automations in Software to the Business world.

Industry Tools

The Traditional way of utilizing Modeling Tools has been in the form coding in specialized Languages or using Modeling Modules available in more sophisticated Tools that allow configuration of Model from a User Interface. These solutions are similar to the Visual Studio version of Modeling, where the underlying codes are automatically written by specifying properties of the Modeling Objects.

Layers of Analytics Stack

The above method of utilizing Tools for Modeling and Information Visualization have been in practice since the last decade or so and has proliferated across Industries and use-cases. In this process of proliferation of such tools the employment gains went to Analysts and Engineers who got trained in using such Tools during their formal Education or via Training on the job. However, the Business Objectives they served remained to be the same.

More so, the stack of Advanced Tools has grown into using Advanced Databases, Specialized Languages and Libraries, and Information Visualization Tools. The choices are many while more Software Companies in the Market went ahead and created their proprietary solutions in these spaces. While some solutions came from Academia and were supported by the Open Source community, many of the solutions remain to be Proprietary.

The Cost of these Proprietary solutions can get prohibitive to a Business Personnel who sees that the real value lies in his/her Data whereas these Tools provide Techniques to mine the right information at the right time from it. The IT Departments gained budgets and mandates from CEOs to spend towards the Advanced Tools, but many are bought without much utility derived out of them. Therefore, the Return on Investment (ROI) is not justified to have and maintain these expensive solutions.

Pentation’s Leadership – Self-Service Insurance Analytics Product for Retention and Cross-sell

Pentation Analytics with its team highly motivated Business professional have the right DNA to make a change to the above-mentioned world of Analytics and make it Cost effective for Companies to have and maintain Business solutions of interest. The unique and special business model that Pentation has is to a Self-Service Analytics Platform – Insurance Analytics Suite™ (Figure 4) using a Combination of Open Source stack and integrations with Proprietary tools that are targeted towards ROI rather than Advanced Tooling.

From Analytics teams to Business Owners

The ROI focus that Pentation has comes from the fact that an all-in-one platform is now made available with just enough of Database technology, Machine learning methods, and Information Visualization available in the Software that is required for Business Solutions to be consumable by the Business User. This also means that the User base would shift from Analytics teams to Business Owners. The Business Owners may come from various verticals, e.g., Underwriting in the Insurance space, or Sales Head in a Retail business.

The Business User is least bothered about the nitty-gritty of the Data Manipulations or Advanced Algorithms. While Graphs and Charts are a natural preference for most Business professionals, even these are not the real end-use that he/she is looking to get. This User’s core focus is the positive Business and Operations Drive that needs to be bolstered from any means possible. Data being at the forefront of Business Analytics requires that this Drive is met by the Data Analytics.

Therefore, the solutions that are focused upon the Business use-case at hand and provides the underlying Data and Modeling layers as Automated options with minimal interface of the Business User with these are expected to be much in line with the Future to come. These solutions would follow Build-Once and Deploy for use Everywhere in the Organization. An Enterprise-wide initiative is needed to adopt such solutions and operate businesses with Automated data-driven recommendations, albeit with enough visibility provided to the Users in the validity of these Recommendations.

Who Leads in the Responsibility?

The Onus of Adoption therefore lies with the Business personnel in the Industry not only to proliferate such solutions across their respective Organizations but also to Co-develop this Platform and the Business Applications that are built off it for the Industry at large. While it is natural for Pentationates to carry the feeling given its Business User orientation, the Industry needs many more adopters of this thought process for proliferation of Self-Service Analytics.

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