“More and more companies, both large and small,” writes David Wagner (@), director of Market development at TeamQuest, “are beginning to utilize big data and associated analysis approaches as a way to gain information to better support their company and serve their customers.” [“The importance of big data analytics in business,” TechRadar, 2 October 2014] Although that sounds like a straightforward concept, Wagner notes that the larger data gets the more difficult it will become to gain insights from it. He explains:
“One study estimated that by 2024, the world’s enterprise servers will annually process the digital equivalent of a stack of books extending more than 4.37 light-years to Alpha Centauri, our closest neighboring star system in the Milky Way Galaxy. That’s a lot of data to gather or analyze — let alone understand!”
Fortunately, every business doesn’t need to analyze all of the data being created, just the data that could have a direct impact on it. The question is: What data do I really need to analyze? Julie Hunt (@juliebhunt), an independent consultant and industry analyst for B2B software solutions, adds, “Numerous misconceptions about big data — what it is, why it matters — make it difficult for organizations to know what to do with it and to understand the business value that can be acquired.” [“How Big Data – and Critical Thinking – Lead to Business Value,” CMS Wire, 13 October 2014] That is why big data analytic projects need to start with critical thinking by humans. Hunt continues:
“[Big data analytic projects will fail] If the right approach and effort aren’t undertaken, if participants don’t understand why they’re working with it, if beneficial strategies aren’t in place. Big data is not just one thing and there’s not just one application for it. If big data analytics aren’t producing good results, it’s not the fault of the big data. Just like working with any data, you have to know what you want to do and why; you have to experiment and learn from the approaches that don’t work; you have to adhere to continuous improvement, identify other needed data sources and so on. It’s a very good idea for organizations to pursue an understanding of big data and where it might produce value for current circumstances and for future direction. Initially an organization may decide that big data doesn’t have a place in current strategies — but strategic thinking should also stretch into when big data analytics should come into play.”
Most analysts have concluded, however, that companies shouldn’t wait too long before deciding when big data analytics should come into play. If they delay, they could find themselves chasing their competition. Hunt reports that Accenture Research examined the impact of analytics on high performance companies and found:
- High performers are five times more likely to aggressively use information and analytics to improve decision making and business performance than lower performers.
- Companies that invest heavily in advanced analytical capabilities outperform the S&P 500 on average by 64 percent.
- Companies that invest heavily in developing analytical skills and adopting an analytical mindset recover quicker from economic downturns.
However, Hunt makes it clear that there is no single approach or group of approaches that suits every business. Some companies can use big data to improve manufacturing processes, others might use it to improve supply chain operations, and still others might use it make their marketing efforts more effective. The question that company executives need to ask is: How can I derive the most value out of big data analytics for the objective I have in mind? She notes that James Kobielus, Senior Program Director of Product Marketing and Big Data Analytics Solutions at IBM, offers the following guidelines for measuring the benefits (i.e., value) of big data analytics as they relate to better customer intelligence and improved customer relationships:
- Volume-based value: The more comprehensive your 360-degree view of customers and the more historical data you have on them, the more insight you can extract from it all and, all things considered, the better decisions you can make in the process of acquiring, retaining, growing and managing those customer relationships.
- Velocity-based value: The more customer data you can ingest rapidly into your big-data platform and the more questions that a user can pose more rapidly against that data (via queries, reports, dashboards, etc.) within a given time period prior, the more likely you are to make the right decision at the right time to achieve your customer relationship management objectives.
- Variety-based value: The more varied customer data you have — from the CRM system, social media, call-center logs, etc. — the more nuanced portrait you have on customer profiles, desires and so on, hence the better-informed decisions you can make in engaging with them.
- Veracity-based value: The more consolidated, conformed, cleansed, consistent current the data you have on customers, the more likely you are to make the right decisions based on the most accurate data.
If you substitute “supplier” for “customer” in most of those sentences, you can see how companies can gain value from improved supply chain visibility. The point is that knowing what you want to measure and what you hope to gain needs to be known before you start worrying about the technology and the analytics. Hunt concludes:
“It’s still very early in the big data story. Still lots to learn, lots of change coming to how analytics are done. Even for organizations with early starts and lots of resources for big data analytics, a great deal of change will come. And around the world, many organizations have barely begun to tap into big data, if at all. The tools of big data also must evolve. Yes, a lot of the work of big data analytics is highly technical and mathematical, requiring sophisticated tools, algorithms and the right experts. But more tools need to be developed for the involvement of business roles in analytics processes and for assessing the validity of the results and the right direction for applications.”
Larry Allen asserts that most of the past efforts in big data have involved collection and storage. It’s time, he says, to take the next step. “In 2015,” he writes, “we will see the evolution in big data companies from solving the ‘how to capture and store’ problem to ‘how to make the data useful.'” [“The Next Step for Big Data,” ClickZ, 12 January 2015] Wagner adds, “Businesses can learn that they can be successful if they’re able to look at the right data in combination with powerful analytics. It all comes back to this: Good data + powerful analytics = better business results.”