How To Leverage Crucial Data And Fine-Tune Internal Processes To Remain Strong In The Face Of Uncertainty And Disruption?

By Aswini Thota, Analytics and AI Leader, Bose Corporation

Using data to make critical decisions is not new. Organizations have been using several strategies to understand the markets, and their customers, optimize internal business processes, etc. What has changed now is the volume and veracity of making data-driven decisions.

A decade ago, organizations used to prioritize storing only their critical data assets. Data organizations used to spend a considerable amount of time designing strategies and solutions to parse unstructured data into a row-by-column format to store data in database tables efficiently.

Limitations in data storage have limited organizations to develop organizations to only store the most important information in an efficient format. However, the availability of big data and innovations in cloud storage have fundamentally changed organizations’ views toward data. With technology innovations such as Hadoop, NoSQL, and cloud computing, organizations started to store the data first and then develop data pipelines to consume the required data.

Below are the four ways modern enterprises are leveraging data to optimize their internal operations:

Descriptive Analytics:

Organizations aspiring to become more analytically driven should establish an excellent descriptive analytics practice. This is the most established form of data analytics. Descriptive analytics uses the historical data stored in enterprise databases to explain what has already happened.

Descriptive analytics can be consumed in several different ways. Business units such as Finance and Sales often use enterprise reports to understand the current state of financial health. Business intelligence is an advanced form of descriptive analytics.

It provides an interactive and ad-hoc capability for decision-makers to slice and dice the data when they want and at the level they want. To implement a good quality business intelligence solution, firms need to collect qualitative data around their Key Performance Indicators (KPIs). Several organizations also implement data warehouses and data lakes to make ad-hoc data analysis efficient.

Predictive Analytics:

Once organizations understand what has happened and study the trends and correlations among different variables, a subsequent natural request from the business leaders is to know how we can predict the event before it happens.

Here, organizations can use statistics and artificial intelligence to develop algorithms to predict future events accurately. Predictive algorithms are sensitive to the quality and quantity of the data. Organizations thinking about predictive analytics should start collecting historical data from different source systems and leveraging cloud platforms.

Prescriptive analytics: Prescriptive analytics is the culmination of data-driven decision-making. It leverages different analytics techniques to recommend a course of action or prescribe solutions to existing business problems. Consider a scenario where you want to generate a sales forecast for the following year.

To generate forecasts, you can leverage your company’s data around historic sales, marketing spending, promotions, etc. But the estimates also depend on several external factors such as competitor pricing, macroeconomics, etc. When implementing prescriptive analytics, organizations need scenario planning capability to quantify risk and have options for different what-if scenarios.

The author is Aswini Thota, Analytics and AI leader at Bose Corporation, who solves organizational and business problems leveraging data