4 Steps to a Big Data Strategy

Is Big Data a business initiative or an IT initiative?  For decades, businesses have been trying to make sense of disorganized transaction data sprinkled throughout the enterprise, relying heavily on human resources to analyze data via data warehouses.  Steps traditionally include collecting, cleaning, conforming, consolidating and organizing the data for business analysts to perform ad-hoc queries to answer key questions such as “How are my sales, by region?” or “How is my inventory, by product?”  Of course, to query data, it must be structured in a data warehouse and browsed via Business Intelligence tools, correct?  Well, not anymore.  Big Data has changed the rules. Before embarking on a new big data project, a policy for handling this data must be in place.  Here are four steps to a big data strategy:

1. Initiate
The first step to a successful big data project is to determine the business objective with a measurable requirement. Careful planning is needed to insure a smooth transition as it involves technical toolsets never experienced by IT or Business before. New policy, procedure, training, project planning and privacy considerations need to be carefully provisioned.

2. Integrate
The power within big data paradigm is its intrinsic ability to process machine- learning algorithms on data from any data source, structured or not. However, contrary to popular belief, big data solutions still require the same techniques used in traditional data warehousing. You must collect relevant data points, prepare the data for analysis and to insure data quality it must be able to align customers, products, employees, and locations regardless of the data source.

3. Optimize
Because the variety and the volume of data we now have the ability to process, the approach to data analysis in a big data environment greatly contrasts traditional methods.  You will need to optimize the right team to perform analysis and refine the system. The paradigm shift to big data also introduces a new role in the corporate organization: the data scientist.  This role requires deep understanding of advanced mathematics, system engineering, data engineering and domain (business) expertise. It’s the responsibility of the data scientist to determine appropriate techniques and algorithms to use to resolve specific business questions.

4. Leverage
Prepare for culture shock as shifts start to occur in business roles, technology solutions and processes. Before a big data project is launched, a strategic readiness test should be performed to assess the adoption of the new paradigm. Business analysts will need to be retrained or repurposed. To accommodate a nearly real-time interaction a new database class, NoSQL, must be introduced. And there will be a shift from reactive analysis (did that campaign work?) to proactive analysis (what should our next campaign offer?), of customer outcomes.

Armed with a complete big data ecosystem, including recommendations created by data scientists, it’s possible to close the loop – feed the results of the analysis into the engine that creates the customer experience: Your website, marketing department, sales force, product development and customer service.  Moreover, the big data machine can now consume recommendations provided as a result of its analytics correlated to new customer behavior patterns and quantify its effectiveness.

As with any new initiative, there’s risk when implementing a new big data project. A tool, a language or a platform alone does not make a solution.