How To Drive Business Value through Smart Data

Data is the most important asset in today’s world. It is rightly termed as the ‘crude oil’ or the ‘gold ore’ of modern times. The main crux lies in the fact that data though voluminous needs to be processed just-in-time for meaningful utilization and consumption. It is fundamental to time-based competition in the market, where businesses compete based on ‘who meaningfully engages the customer first’.

Data Smart Data is the data in ‘the processed form’. It is the data that can be termed as ‘refined petroleum oil’ or the ‘24 carat glittering gold’, which can be utilized to derive business insights and value. This utilization can be in many forms – right from quickly engaging the customer to prompt further purchase or to urgently take business decisions to replenish stocks.

In this fast paced world, raw data that is even a few days old may be stale. To derive business value quickly from the data, which may be unstructured and voluminous at the same time, an enterprise needs to have the right machinery in place that too functioning at top speed. This is the building block required in an enterprise for delivering quick personalized experience to the customer in order to drive sales.

To derive and drive business value from raw data for business consumption, the enterprise needs to establish the smart data framework, which is flexible and scalable at the same time. 

Following are some building blocks based on which an enterprise can build its Smart Data framework for driving business value:

• Build the data strategy and data governance team - Build the data strategy, to begin with, and carefully design the data governance team. Strategy is the DNA of any project. A clearly defined strategy for making data discovery, which in turn can fuel further data projects, is more than half the battle won for any Smart Data project.
  
•  Having a data strategy and the data governance in place is quintessential before making investments, such as building massive data lakes. Huge amounts of different types of data (structure, unstructured, and semi-structured) that is required to be processed and analyzed at high speed needs data lakes as against data warehouses that were used till a few years ago. This requires the right investment at the right time.
  
• A core data governance team should also be instituted that is responsible for taking collective responsible decisions in time. It speeds up the strategy evolution process. With the core governance team in place, it is much easier to incrementally build on the data strategy and the data governance aspects first than aligning them later when infrastructure is procured and investments are made.
  
• Retain archival data & dig deeper in streaming data - Keep at least two years of data, which is gathered from trends related to customer’s purchase and the relevant records, for analysis. It gives considerable perspective about the customer and his/her spending pattern. It also enables the enterprise to derive smart inputs for prompting the customers throughout their purchase cycle.

In addition, analyze the streaming data as it comes. This is a rich source for tapping customer behavior and further analysis to derive Smart Data. Based on this Smart Data, the enterprise can engage the customer with just-in-time sales and drive sales – in the customer’s current buying cycle as well as their buying cycle at a later date. This Smart Automation is what transforms the plain data into Smart Data.

Deploy Open Source Big Data platform

Install an Open Source based Big Data platform. Open Source is highly scalable. Scalability of the platform is the primary requirement for processing streaming data and acting on it impromptu. 

It offers suitable solutions to enterprises, who want to churn huge voluminous data that is dissimilar and surfaces in various combinations (structured, unstructured, and semi-structured). This voluminous data needs to be analyzed spontaneously in real-time or may have to be stored for later analysis – hence the requirement of open source big data platform for deriving just-in-time business value.

Employ metadata

Utilize metadata features to the fullest. Huge volume of data needs to be parked before analysis and consumption. Hence the data needs to be searchable. Using metadata features makes the data highly searchable and consumable for the end-users who have the right role and permission. Metadata makes plain data agile and smarter and allows faster churn into Smart Data for deriving quick business value.

Employ Machine Learning and Analytics

Make ample use of machine learning based analytics in order to handle business requirements, such as Personalization and Merchandising. The technology enables the enterprise to enhance their business workflow and deliver personalized experience and information to the customer about similar purchased items through the mobile platform.

This technology helps to make the available data smarter by finding patterns in the raw data. Predictive Analytics makes use of available raw data and imparts the smartness to detect frauds, predict buyer/seller risk, discover patterns, and find anomalies as well as insights thus helping the enterprise to save millions.

While using machine learning based analytics, the enterprise should fine tune their businesses in order to discover new ways for data storage and analysis and find solutions for queries that were difficult to resolve earlier.

Engage a data partner

Utilize the services of data solutions providers for assistance in deploying the open source solutions for enterprise data management and analytics. These data partners, with their decades of experience in resolving data related issues of enterprises, tailor a personalized solution that suits the needs of the enterprise and their end-customers. interoperable enterprise data management and analytics solutions provide a high ROI. At the same time, they bring forth avenues to quickly churn smart data for deriving business value for driving just-in-time sales.

The data solution providers help in selecting and deploying the right kind of interoperable tools and technology. their expertise helps ease the integration woes and maintain the resultant smart data ecosystem. These data solution providers, with their experience in empowering enterprises to deliver scale, help in optimizing the results by using open source solutions and dashboards with useful visualizations at a glance.

Smart Data is the way ahead for driving business value through just-in-time sales while motivating customers during their purchase cycles. Transformation of raw data into smart data, which is highly consumable, is a function of deploying the right EDM and analytics solutions that are interoperable with the existing enterprise systems.