Advocacy

Keeping Big Data from Getting Too Big

Yes, you can have too much of a good thing. That’s a problem faced today by many business managers who are swimming in a flood of data.

The right data can, of course, help to increase revenue and improve products and services. But incomplete data can cause problems for sales and marketing programs. Without proper analysis, even the “right” data can be worse than no data at all.

Data – whether it’s big or small – has become a foundation for marketing. You cannot be a successful marketer or commercial organization without having an understanding of data and analytics.

“In today’s day and age, we are getting much more data than we know what to do with,” says Umesh Patel, General Manager for Canada of Eaton’s Power Quality Business. “If we don’t have competencies around data analytics, we have the risk of reaching incorrect conclusions.”

We spoke about this issue with Patel and with Magdalena Kotek, a marketing executive in Hong Kong who is former CMO for GE Capital Global Financial Solutions.

“Data – whether it’s big or small – has become a foundation for marketing,” says Kotek. “You cannot be a successful marketer or commercial organization without having an understanding of data and analytics.”

Kotek notes that the CMO’s job is to help CEOs meet their business objectives “and one of the most powerful ways to do that is by understanding the data that flows through our organizations for sales, customers and markets.” Like Patel, Kotek says organizations need to figure out the right data and analyses to solve the company’s challenges.

“How do I create clarity in all that [data] to figure out the important things that I want to measure while not losing sight of the things that might disrupt my business?,” she asks. “You really have to be able to identify critical commercial questions, and be thoughtful about picking analyses that are going to make a difference for you and will result in insights and action. The challenge will grow as organizations incorporate data from social media and mobile technologies to understand and impact customer sentiment and buying decisions.”

A Universal Challenge                                                                                        

Most companies today face these same challenges. Even at global giants like GE Capital and Eaton, executives and technologists are constantly trying to find a better way forward. This is largely due to the explosion in data over the past few years. More than half the data from all of history was created in just the past two years, according to sources cited in “Accelerate How You Innovate: Data Center Evolution in the Era of the Cloud,” recently published by the Transform to Better Perform initiative.

All that data has been choking traditional data centers, leading many companies to look towards the cloud and other newer technologies to augment on-premises facilities. Together with new built-in-the-cloud applications, companies are gradually learning how to stem the tide of big data. In turn, that helps business managers access the data they need to do their jobs with greater agility and authority.

“I think there’s a lot more that we could be doing,” says Patel, echoing a sentiment expressed by many of the business managers we’ve interviewed. “It will come about. It will be a matter of us getting more comfortable with it and finally getting there.”

For example, Patel said he would like to have the right data at his fingertips, ready for analysis in one single application. “What I mean by that is that today I have to go out and gather data from multiple sources. That’s the lion’s share of the effort – prior to even getting into analysis,” he says. “I want it so that I can spend my time not gathering data but rather analyzing it.”

Today, he says, his company’s technology is a “mish-mosh” of Enterprise Resource Planning (ERP) applications with “another hundred applications” around them. These include warehouse management systems, legacy order management systems, quotation tools and “a lot of different things that are not necessarily part of the ERP.”

That scenario will be changing at most companies as the big ERP vendors like SAP and Oracle shift their applications to the cloud, where many IT teams are already building their own applications in hours or days instead of weeks or months that it takes in traditional technologies.  Eaton is slowly moving towards those technologies, too. “I would say we are relatively conservative in our approach, and that is because there are legitimate concerns around security risks and privacy risks,” he says.

The DNA of Innovation

Like Eaton, GE is a company built on innovation and engineering. While Kotek worked in its financial arm, the company is renowned globally as a leading manufacturer of sophisticated equipment including jet engines and medical imaging systems.  “I think the DNA of having scientists and engineers is really helpful,” said Kotek. “GE is a company that started with an inventor – Thomas Edison.”

She said back around 2005, gathering data was harder because it had to be pulled manually. By the time business leaders got it, insights could lag the market. “Over the last 10 years, cloud computing solutions like Salesforce.com have been a disrupter and have provided easier access to near-time data and analytics. Also in the financial services industry, increased regulation, has required companies to have a single view of the customer across databases and functions.”

GE is also a company that was ahead of the curve when it came to agility. “We know that to be successful in any market you need to be fast. In order to be fast, you’ve got to be simple,” she says. “GE has simplified processes and invested in technology to gather insights and make decisions faster.  Along with GE, Google, Amazon, and the next generation of new technology startups and tools will continue to expand the boundaries of big data’s impact.” Agility is probably the primary reason that companies today are blending the technologies they’ve used in the past with the cloud-enabled data centers of tomorrow, a transformation aimed at keeping big data from getting too big.