Did you go online first thing this morning? Tap and swipe your devices throughout the day? Make a final run through before turning in for the night? More and more people are constantly connected to the Internet and to one another via mobile devices – and it’s having a major impact on consumer buying habits. Like oxygen to a scuba diver, the Internet has become the life force of today’s consumers. Mobile devices and online marketplaces are now a seamless part of the consumer buying experience. Consumers are searching online for products and bargains and will share their impressions in an instant on social media.
In this highly digital world, big data is playing an ever-increasing role in the fundamental business strategies of consumer products (CP) companies who are trying to reach and engage always-on consumers. Having a strategy to determine how best to manage the growing availability, granularity, and velocity of data is becoming a top concern for many organizations.
How is your organization approaching its enterprise data strategy? How well is that strategy evolving to not only accommodate the changing definition of big data, but the ongoing needs of the business? And how can you take a more strategic approach to data acquisition and data management than perhaps you have in the past?
These are pressing questions that are demanding attention among CP manufacturers who are looking more closely at data analytics and demand signal management as a means to beat fierce competition. A recent industry benchmark study polled more than 60 executives at CP companies to see where their organizations are on the “big data” maturity curve. The study looks at how CP companies are capturing data, aggregating it, harmonizing it, and ultimately leveraging it inside their organizations to respond more effectively to market-place demand.
The upshot of the study? CP companies need to consider how to adapt to the evolving definition and role of big data while continuing to meet specific business needs for data analytics and insight.
Facilitate a strategic discussion about enterprise data
Beyond quantitative data from business transactions, organizations need to rationalize qualitative data such as social media analysis and unstructured content from documents. Add to that data from the Internet of Things, sensors, machine learning, and more and the importance of a mature enterprise data strategy becomes quite clear.
It’s time for the IT organization to facilitate a more strategic discussion around the nature of enterprise data to gain an understanding of the critical requirements for different business functions. With this insight, IT can build an enterprise data strategy that aligns with and supports those needs.
Establish one data set to meet multiple needs
A comprehensive enterprise data strategy can help your organization establish one data set that meets the needs of different audiences – each in its own way. Not only is this approach of strategic value to the organization, it avoids the risk of duplicate data and investing in the same data set twice.
Consider point-of-sale data. This type of data is relevant in different ways to different business functions, such as sales, supply chain, and marketing. The sales organization would look at point-of-sale data from retailers to see what consumers are actually buying. It can look at the orders and shipments placed by the retailer and compare it to what is shipped and actual consumer sell-through. Sales can immediately see if there’s a large discrepancy between forecasted volume and what consumers are purchasing.
If there’s extra inventory, then sales may expect a high number of returns. Conversely, if consumers are buying more than anticipated, then sales may recommend that the manufacturer ramp up inventory or adjust the parameters of a promotion so it can accommodate higher demand.
The supply chain team would look at point-of-sale data in a very different way. It would seek answers to questions such as: How does actual consumer sell-through impact our short-, medium-, and long-term forecast? Does this impact whether we locate our finished goods inventory in our warehouse, a retail distribution center, or a retailer’s store shelf? Do we need to procure additional raw materials over the long term as manufacturing inputs or packaging? Does this impact our forecast for future demand so we can get the right materials to ensure the availability of finished goods inventory over time?
Marketing may look at point-of-sale data to measure the effectiveness of promotional campaigns. If it can correlate demand and track it back to the timing of the campaign, it can see if there’s an increase in sales volume to determine if the campaign was successful and worth repeating.
This simple example demonstrates how one data element is relevant to three different audiences in related but completely different ways.
Become a strategic partner to the business
If you expand this example to encompass all of the myriad forms of data available to CP companies, and the various needs for data analysis across the enterprise, you can see the value in being able to manage it holistically in an enterprise data strategy. That is – bring the data in, manage it once in your enterprise data environment, and then be able to provision it in a way that’s meaningful for your various audiences depending on their KPIs and objectives.
When you’re able to do that, you’re able to be a strategic partner to the business. You’re able to deliver the data to different stakeholders in a way that’s compliant with your IT policies, minimizes the risk of data duplication, and improves data quality.
What is your strategy for enterprise data? Are you keeping pace with the evolving definition of big data? More importantly, are you leveraging big data to reach, engage, and serve your consumers with timely, tailored, and relevant brand experiences along the entire path to purchase and beyond? To explore this topic more fully and gain insight into a tool that can help you climb the big data maturity curve, consider the SAP Demand Signal Management application. It enables you to capture external market and retailer data in real time – and combine it with internal business data and state-of-the-art analytics – to sense, assess, and respond to demand signals faster than ever before.
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