Promises and Perils of Big Data
Leading users of Big Data set a high bar for success. But how can your company profit from Big Data? The first step is learning how to distinguish the actual potential from the extravagant claims. Read below the perils associated with each of the promises:
Promise 1: The technology will identify business opportunities all by itself.
Peril 1: Failed technology deployments often start with the assumption that the shiny new tool will generate value all by itself. Successful companies start by applying advanced analytics to solve a small number of high-value business problems with in-house data before investing in technology. In the process they gain insight into operational challenges, understanding the limitations of their data and technology. They can then define the requirements for their Big Data technology solution based on an understanding of their actual needs.
Promise 2: Harvesting more data will automatically generate more value.
Peril 2: The temptation to acquire and mine new data sets has intensified with the explosion of social media and mobile devices. And yet many large organizations are already drowning in data, much of it held in silos where it cannot easily be accessed, organized, linked or interrogated. Big Data journeys can only be successful if the organization’s existing data is given priority. From an analytical perspective, it is generally easier to work with data that has some history than it is to attack brand-new data sets.
Promise 3: Good data scientists will find value for you, no matter how your company is organized.
Peril 3: In order to profit consistently from Big Data, you need to create an operating model that harnesses the power of the data and advanced analytics in a repeatable manner. Successful data-driven businesses align their organization, processes, systems and capabilities to make better business decisions based on the insights from their data and analytics teams.
The Big Data revolution has already disrupted many industries. Companies that realize the promise of customer data analytics tend to follow three rules:
- Apply advanced analytics to solve a few high-value business problems before investing in Big Data technology solutions.
- Create value from your in-house data before expanding to new data sources. Then use test-and-learn approaches to inject forward-looking data sets into your historical data.
- Align your operating model to enable your organization, particularly the front line, to act quickly and with confidence on the insights from your advanced analytics teams.
Companies that follow these rules will be better positioned for success in the age of Big Data.