News / Features



Small business, big data

Surprisingly not an oxymoron, many small and midsize businesses are dealing with big data issues too 

By Mark Troester, Global Product Marketing Manager, SAS

Is your business too small to worry about "big data?" Have you heard the volume, variety, velocity descriptors about big data and thought, "Nah, that doesn't apply to me"?

Think again – and consider this simple and accurate definition for big data: When volume, velocity and variety of data exceed an organization's storage or compute capacity for accurate and timely decision making.

Clearly, big data is a relative term. Every organization has a tipping point, and most organizations – regardless of size – will eventually reach a point where the volume, variety and velocity of their data will be something that they have to address.

More importantly, every organization has an opportunity to leverage big data to its advantage – to drive accurate and timely decisions that can materially affect its business and organizational goals. When you tackle big data with big analytics, you quickly realize that big data presents an opportunity for every organization. Big data is not just for multinationals. And it's definitely not one-size-fits-all.

Whether your revenues are $1 million or $100 billion, knowing how to manage and analyze data is critical to success, as the Economist Intelligence Unit research illustrates well in its recent study Big Data: Harnessing a Game-Changing Asset . Nearly half the survey respondents who listed big data as a major issue facing their organization reported revenues of $500 million or less.

" The bottom line for organizations of all sizes: You should not be doing less sophisticated analysis just because you have more data. "

Among the report's findings:

  • Over the last year, 73 percent of survey respondents say their collection of data has increased "somewhat" or "significantly."
  • Companies self-identified as "strategic data managers" – those with a well-defined data management strategy that focuses resources on collecting and analyzing the most valuable data – tend to financially outperform their competition more than others – 53 percent, compared with 36 percent.
  • Thirty-two percent of self-identified "data wasters" say they lag behind their peers on financial performance. Only 1 percent of strategic data users report that.
  • More than half of companies report that they expect the increased volume of data to improve operations. The second most popular answer (respondents could choose two): 36 percent expect it to inform strategic decisions.

How is your industry affected?

It's easy to think that only certain industries generate a lot of data or deal with new data types. For instance, retailers get a lot of SKU data and information from their supply chains. Financial institutions are constantly monitoring the inflow and outflow of money. But would you think a small, regional utility company might have big data concerns?

As utility companies of all sizes start to use smart meters, they can better forecast load and reduce the need to build additional plants. But what are the data implications of smart meters? With a smart meter, a utility goes from collecting one data point a month per customer to receiving 3,000 data points for each customer each month, while smart meters send usage information up to four times an hour.

One small Midwestern utility is using smart meter data to structure conservation programs that analyze existing usage to forecast future use, price usage based on demand and share that information with customers who might decide to forestall doing that load of wash until they can pay for it at the nonpeak price.

A regional trucking company provides another example. Global position satellite technology now allows firms to track the trucks, the merchandise – practically anything you can attach an RFID tag to. How a company uses that information – to reroute trucks to create efficient routes, alert customers to deliveries, and forecast and price services – depends on the ability to manage and analyze data effectively.

Fast-growing, regional restaurant chains are also affected by big data. If you own one of these restaurants, what can you do about an onslaught of negative online reviews? Do you have the capacity to analyze the comments made about your restaurants on Facebook or Yelp? As the Economist Intelligence Unit report notes, "Each time new kinds of data are born, so too are opportunities to learn from them, combine them with existing data and create new insights."

In another example, a health care consultancy has made the data coming out of medical practices the focus of its thriving business. The company collects billing and diagnostic code data from 10,000 doctors on a daily, weekly and monthly basis to create a virtual clinical integration model. The physician practices whose data is being collected have agreed to be measured against 90 standards of care guidelines. This allows the independent practices to meet Federal Trade Commission guidelines for negotiating with health plans. The consulting company analyzes the data to help the groups understand how well they are meeting the guidelines and whether they qualify for enhanced reimbursement based on offering a more cost-effective standard of care. It also sends them automated information to better take care of patients, like creating an automated outbound calling system for pediatric patients who were up to date on their vaccinations.

More and more companies like this one are building their business models on the analysis of data. So it's not just about running your business better with big data – in many cases, the data is the lifeblood of the organization.

IT considerations – big and small

Whether you're in health care or the service industry, you need to start thinking about the requirements and design for your analytics projects. As your data grows, so do your IT requirements and – oftentimes – the gap between the business need and the IT infrastructure. To overcome these challenges, consider these points:

  • It's not size that matters. While it's interesting for the technical discussion to focus on size, the focus should be on business value first. Identify your business challenge or goal. Do you have a need to leverage blog and social media data to analyze customer churn? Do you need to strengthen your fraud analysis approach by mining clickstream and other forms of content? Do you need to analyze many data points at the customer transaction level? Focus on the business value so you can align your goals with your technical and solution approach.
  • Think about a different kind of big. From a design perspective, think about the big picture. You certainly don't need to take a big bang approach in terms of implementation, but leverage standard architecture principles to ensure that you don't box yourself in.
  • Look beyond the hype. If you do much research on big data, you're bound to run across a lot of articles on Hadoop. This new software framework for big data is getting a lot of attention, and it's a great technology, but it is not a realistic solution for SMBs and midsize companies. However, just because Hadoop isn't for you doesn't mean big data is irrelevant altogether. Consider what is best for your organizational growth before you invest purely based on price or hype.
  • Analytics is the key. In most cases, we think about leveraging information management technologies like data integration and data quality to prepare data for analytics. Although this is certainly an important step, the biggest differentiator will be how you can apply analytics to determine what to do with your organizational data, determine which data is relevant, and how or whether data should be stored.
  • Resources are scarce. Lack of resources, especially the right resources to analyze big data, is critical. In the Economist Intelligence Unit research, lack of the right skills to manage data effectively is among the top two challenges cited by survey respondents (30 percent), followed closely by "We can't get the data to the right people in the organization" (23 percent) and "We don't have the analytic skills to know how to use the data effectively" (22 percent). But using data doesn't require hiring a team. In fact, many successful companies start by looking internally for the people who are always asking questions that everyone wishes they had an answer to and pairing them with a statistician who can help them learn to dig into the data. In "How to Get Started with Analytics" we offer suggestions on how to deploy your company's internal assets to take advantage of big data.

Solving your big data problems with robust analytics

The bottom line for organizations of all sizes: You should not be doing less sophisticated analysis just because you have more data. If the size of the data is choking your analytics, the problem is not that you have too much data. It's that you don't have the right analytics environment.

This new big data world is not only about running problems faster, but about solving problems that were not solvable before. As data volumes grow and new data sources continue to multiply as well, what new big data problems do you have? When you put the right analytics to work on your big data problems, you can stop thinking of big data as only a challenge and start seeing big data as an opportunity.

Bio: Mark Troester oversees the company's market strategy efforts for information management and for the overall CIO and IT vision. He began his career in IT and has worked in product management and product marketing for a number of Silicon Valley start-ups and established software companies.