We often hear experts on Big Data talk about how Big Data powers business transformation. Indeed an effective Big Data strategy that integrates and services various business functions with the objective of raising revenues, reducing risks, and bringing down costs can truly bring successful reform and sustainable profitability to a business. The key here is integration and developing a strategy that looks at each business function both separately and as a whole.
Many businesses use Big Data to improve their sales and marketing function. Big Data insights helps them acquire more customers, while better retaining current ones, boosts store traffic, conversion rates, and improves advertising effectiveness. They often do all this without looking at the business as a whole and though such a strategy can definitely improve the bottom line, in the long run the effects may start to slow down.
What’s critical for long term financial success therefore is to try and develop a Big Data strategy that encompasses all the critical business functions. This will help bring efficiency and improved performance to all areas of operations by optimizing network performance, predicting utilization/capacity, maintaining a more efficient supply chain and consolidating suppliers, while also hiring and retaining the most effective workforce.
As per a study by TCS (http://sites.tcs.com/big-data-study/big-data-benefits-challenges/) activities, which companies believe have the greatest potential for Big Data benefits, go far beyond marketing and sales. In fact, of the 25 highest-rated activities, there are an equal number in logistics and sales (six). In addition, marketing and customer service had four each.
Over a series of articles, we will write in the next few weeks, we will talk about how Big Data initiatives can be implemented across various functions in an organization, the main ones being:
- Supply Chain
However before we begin any Big Data initiative there are some critical steps businesses need to follow. Let’s quickly outline them:
1. Define responsibilities, build a team: Designate who is responsible for collecting data (IT and analytics team), who identifies areas within their respective functions where big data could drive value and ofcourse the Big Data Team.
2. Identify what data is worth analyzing: Valuable business insight can come from many sources. It is important to explore these sources and see which of them are viable and can answer the business problem and add value to the Big Data strategy.
3. Match big data with business functions: Some big data programmes can be implemented in a variety of settings, but many will need to be matched to specific functions.
4. Assess the IT architecture: Ensure that your businesses information architecture can accommodate massive, high-speed, variable data flows.