Don’t make big data stand alone, warns Gartner
Gartner cautions organisations against treating big data as unique and criticises business intelligence suppliers for failing to exploit mobility
Gartner is warning organisations against treating big data as a unique species. The analyst firm also sees risk in neglecting what is special about mobile as against desktop computing and sees genuine chances for businesses to convert their data into cash.
Speaking ahead of next week’s co-located BI & Analytics and Master Data Management summits in Barcelona, Gartner analyst Ted Friedman advised organisations: “Do not make your big data implementations siloed. Make them part of the overall strategy for BI.”
In a press statement ahead of the summits, Gartner said: “While IT organisations conduct trials over the next few years, especially with Hadoop-enabled database management system (DBMS) products and appliances, application providers will go one step further and embed purpose-built, Hadoop-based analysis functions within packaged applications. The trend is most noticeable so far with cloud-based packaged application offerings".
Friedman said: “Link to stuff you are already doing. Don’t make big data a standalone thing. And don’t feel like you’ve got to go out and buy a whole new technology stack.
“As a governance and MDM specialist, I worry about organisations thinking big data is off to the side and that they are not going to leverage competencies built up over the years in BI and data governance and quality.
Read more about big data strategy
- Effective 'big data' strategy helps advertising firm attract clients
- BI strategies evolve for big data analysis needs
- 'Big data' sprawl forces new thinking on data warehouse strategies
“There is a risk there, especially with IT budgets going outside IT to the chief marketing officer.”
Friedman confirmed Gartner is seeing this with clients. “They’ll say: ‘We’ve got a project in marketing deploying Hadoop and advanced analytics.’ And you find out they are not using the data integration tools they already have, relating to their existing data warehouse. It all feels disconnected. And it does bother me.”
Big data also imposes a mandate for organisations to get better at governing data, he said. “Social media data, for instance, raises the bar on good practices with respect to the legal and ethical rights to use data the way you might think you do,” said Friedman.
Unstructured information, such as social media, will be among the most difficult but also the most rewarding of big data types, he said.
“Data variety is the most important of the Vs [volume and velocity is the third]. The most powerful and impactful information use cases will be those that combine diverse kinds of data – the ability to make sense of less structured stuff, and figure out how that relates to structured transactional data. But organisations are still weak there. Text and content analytics are still young areas.”
Small mobile form factor distracts BI from voice
Gartner’s pre-summit statement said of mobile: ‘In their rush to port their applications to mobile and tablet devices, BI vendors have tended to focus only on adapting their traditional BI point-and-click and drag-and-drop user interfaces to touch-based interfaces’.
Friedman said he see parallels with client-server in the 1990s: “People were not taking advantage of the interactivity and flexibility that the PC brought over the mainframe. It’s the same with mobility today. It’s no use just re-engineering what works on the desktop”. Voice-enabled analytics is being missed, he said.
Cash for data
Gartner is predicting that 30% of businesses will be monetising their information assets directly by 2016. It is arguing that big organisations will defray their big data costs by turning their own information into revenue and that a new breed of information providers will emerge.
Friedman said he was coming across Gartner clients in non-information businesses with job titles such as "product line manager, information".
“It’s part of the second half of the information age. The first was to leverage data to make internal operations more effective and efficient. The second is information as a business strategy, as a line of business,” said Friedman.
