Companies now have the ability to obtain massive amounts of information about their customers, their markets, their products and their competition, says private equity investor and Co-Founder of MarginXL Capital Partners, George Stelling. This wealth of data can greatly increase a business’s productivity and profitability, but only if businesses understand how to harness the data and glean insights from it.
Since 2009, the amount of data that companies can accumulate and analyze has been growing exponentially. Now, this factor of production is just as important as labor and capital. According to George Stelling, to fully harness ’data as a new asset’, companies need to formalize their analytics and data strategies with clear objectives and methods. When aligned, firms need to experiment, iterate, and look carefully at insights that can impact near every process and activity in the company.
“Big Data” often defined as a collection of structured and unstructured data that requires specialized management due to its large size and complexity, can speed up decision making and improve the quality of decisions executives and line employees make. As George Stelling, a longtime speaker at the ModelN Rainmaker conference, explains, “The ability to analyze data and glean insights allows companies to take actions on everything from inventory turns to patterns in employee productivity. This information allows organizations to better align business goals with overall company operations so customer satisfaction improves, costs go down, and product features are honed”.
A study by IBM’s Institute for Business Value (IBV) supports these findings. IBV surveyed 900 business and IT leaders from 70 different countries concerning their use of data analytics. Their main findings are that there are nine “levers” that organizations must handle and coordinate in order to take full advantage of big data programs. These levers are based on demonstrable results from companies’ data analytics initiatives. They can be applied effectively, no matter the size of the data pool or the scale or scope of a specific project.
First, a company must promote the availability and use of data and analytics. Big data may mean companies must enhance their infrastructure to allow for oversight of data as an asset and to ensure its security, timeliness, and accuracy. That overhaul may require hiring more people, directly or as contractors, with expertise in data management and analytics.
Additionally, big data must be viewed in the same manner as any other asset. “A company should arrange for employees and data analytics experts to have access to the right tools and processes need to be developed to ensure that insights are not only identified, but actioned routinely”. Organizations should develop ways to measure the impact of analytics on their efforts and track performance of their investments.
From there, companies should be able to forecast the impact of big data on future business strategies. Based on that information, organizations can see what insight, actions, and implementation tactics provide the best results and reinvest back into those specific initiatives. Management and other stakeholders should understand the necessity of piloting ideas and testing them routinely. From here, the company can transform itself from one that is reactive to more of a data-driven company and become more profitable in the process.
Bob Charles, co-founder of MarginXL Capital Partners adds, “When implementing a new data analytics program, it is vital that companies manage these program levers all at once and in a coordinated fashion”. As the IBV study states, “There is a strong correlation between organizations that excel at these levers and those that create the greatest value from analytics. The levers...are interrelated. Taken individually, each of the levers does not equal one-ninth of the solution. Organizations that invest in these nine levers–with particular attention to the symbiotic relationships that exist–can accelerate value creation, simplify analytics implementation and realize value from analytic investments.”
There are valuable profit expansion opportunities that justify the considerable focus and effort to set up big data programs with well architected analytical processes, Bob Charles said. Done well, big data and advanced analytical programs can allow companies to segment their markets as never before, targeting advertising and marketing funds to their fullest potential. Data analytics can greatly improve real-time decision making and speed up the entire enterprise. With current information, management can quickly respond to changing market or corporate conditions and capture profits ahead of their less nimble competitors.
Big data and analytics also help provide valuable insights into the next generations of products and services. With the ability to gather and collate information on real consumer needs and current uses, companies can create new products and services or create derivative products and services that are more tailored for specific customer segments and markets.
Companies that embrace big data and analytics early can generate substantial competitive advantages over firms using traditional data infrastructures and strategic planning approaches. Harnessing big data and advanced analytical tools can also boost productivity across the organization by providing insights into what’s important and what’s not to customers. With traditional forecasting and strategic planning, decision makers make educated guesses about customer and market trends and allocate resources to respond to those received needs. With “nowcasting,” decisions makers don’t have to rely on a static model of customer and market needs, but can use real time information to respond to customer and market demands with a higher probability of being successful.
The rise of big data and advanced analytics is also creating new roles and employments opportunities across the US and the world. In fact, there is now a shortage of data analytics experts to help with the definition, execution, interpretation, and implementation of these programs. By some estimates, the U.S. could require as many as 190,000 new ‘data scientists’ by 2018.
George Stelling and Bob Charles emphasize that while defining big data programs, architecting the right data processes, and building the infrastructure needed is time consuming, the potential payoffs in improved customer satisfaction, profits, employee productivity, and strategic alignment make these programs a sound investment.