We generate nearly 2.5 quintillion bytes of data every day, more than 80 percent of which is unstructured data. Multiple industry reports and research surveys highlight the fact that only a handful of C-level executives have made effective use of this data, while the rest are still trying to cope with data overload.
However, Forrester Research predicts a 350 percent revenue growth - to $1.2 trillion by 2020 - for public companies that have embedded analytics and insights-driven action into their company DNA.
In our previous article, we presented our perspectives on how to understand your organization’s requirement for speed, including key considerations to improve the speed of your organization to meet the speed of business decisions. But speed in itself cannot create business value. It has to be complemented with actions.
So how do you ensure your organization has the right capabilities to drive faster and more effective business actions? There are three key considerations for understanding the correlation between data, speed and business actions:
1) Actions should guide your enterprise data requirements - not the reverse
Data in itself is not actionable, even when it is real-time, at hyperspeed. To drive effective business actions, data needs to be sourced, processed and shared with the end goal in mind.
Highly effective organizations ensure all data delivered to decision makers is relevant, timely, and actionable. It is important to strike the right balance between strategic foresight and analytical hindsight. The business world today is focused on “hypothesis-driven insights approach.” But is it enough?
To realize true value from data, this must be embedded in an iterative “test-and-learn” cycle:
- Start with defining key actions that need to be taken based on your organization's strategic objectives.
- Develop a set of hypothesis on potential levers that influence the actions and their effectiveness.
- Define the variables and design algorithms.
- Continuously refine each variable in an attempt to derive meaningful, timely and the most relevant insights.
The speed and frequency of the test-and-learn cycles, relative to your competition, will determine the ultimate impact of your analytics capability. Further speed can be achieved by minimizing the human element and investing in machine learning algorithms.
2) Data is ubiquitous, but insight is scarce – be judicious in your data strategy
Once you have identified your data requirements, you must determine how to best extract insight from this data. The decisions you make around data sources, collection methods, linkages, and structure will drastically influence the value of the output. Consider the following three aspects when designing your data strategy.
Leverage your ecosystem
The data that you require may not reside within your organization’s four walls. Strategic partners, suppliers, and customers represent a wealth of untapped potential. Consider the case of designing a new product. While the internal view of sales numbers is essential, the design can be enriched through incorporation of unstructured customer sentiment from social media, as well as raw material forecasts from your suppliers. Information is an asset that appreciates in value as it is shared. Take advantage of this.
As an example, Caterpillar, an industrial equipment manufacturer, enforces data sharing agreements with each of its dealers. In return for the customer data, it provides the dealers with benchmarks and other insights to help increase their sales efficiency.
Maintain cruising altitude
Decide when a thirty-thousand foot view is sufficient and when additional layers of granularity are necessary. More details often mean higher costs and increased complexity, which has a negative effect on the value and speed of the insight generated. On the other hand, a lack of detail may impact the accuracy of your conclusions. Consider these trade-offs carefully and refine your decisions relentlessly.
Relate the unrelated
First degree connections, such as the relation of traffic data to supply routes, is useful but commonplace. The real value lies in tying together seemingly unrelated concepts, such as weather patterns or political unrest with a broader supply chain optimization strategy. Challenge yourself to go beyond the obvious.
As an example, consider how VineSleuth effectively leverages a collective set of variables to boost retail sales of wine. They recently revealed a Sommelier-as-a-service technology that provides end customers with tailored wine recommendations directly in the aisle. Wine is characterized using sensory science along with analytics on customer’s personal preference, combination with chosen meal and their budget.
Another great example is the Coca-Cola Company, which combines its social media analytics with weather prediction to determine the mix of brands to be sold each and every day.
3) Data is of no use without action – ensure your organization is ready to act
Organizations today have more data than ever but struggle to bring the right data to the right teams at the right time to support business actions. With 2.5 quintillion bytes of data being generated every day, perfection is far-fetched. While ‘good’ companies are adept at asking the right questions and generating relevant insight to support their conclusions, ‘great’ companies take it a step further, delivering this insight to the right teams and conditioning them to rely on it for decision making.
This is achieved by cultivating a data-driven culture and demanding more than gut-instinct as justification for key business actions. We use the term “culture” to indicate that this goes far beyond a simple governance model with well-defined responsibilities. The organization must foster and reward data-driven decision making at every level in order for the impact of analytical insight to be felt on the front lines and on the bottom line.
Unlocking the immense potential of data, needs a clear understanding of the organization’s need for speed and complementing it with business actions. In order for data, speed and action to co-relate and co-exist, your organization needs an action-driven data strategy and a strong data-driven culture.
(The authors would like to thank Andrew Liu for his contributions to this column.)
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