Does big data really have business value? Sure, it does – if you’re using it right. And you have the right tools in place.
“Analytics can be an area of blind faith,” notes Nick Hardiman in a Tech Republic article. “Not getting clear business goals for the data set can result in an attitude of “hope something good pops out.”
So, we’re going to tackle the subject of determining business value with big data in today’s post.
1. Determine what your company needs from big data.
A good place to start is in the requests your business units send to IT. What are the different managers and teams looking for in reports, regular reviews of social feedback and in the lead conversion process? Big data can provide real insights for your business, but it won’t do the job for you.
2. Start small and focus on “quick wins.”
Sushil Pramanick recommends setting up a big data lab before you roll out to your entire organization.
In this process, he recommends a few objectives for your “lab” that include “testing a few different big data analytics technologies for two to three months and “two to three quick wins to demonstrate the value of these technologies from both an IT and business perspective.”
Quick wins help you see the real business value of big data on a small scale. This can do three things for your organization:
- Help you evaluate which technologies are best for your organization
- Develop buy-in from all the stakeholders
- Determine who will use big data technologies in their teams and projects
3. Focus on operational outcomes.
“Data liberation leads to new technologies and new approaches to data, which opens up new business scenarios by extracting insights for decision making and operational efficiency that were not previously available,” notes Svetlana Sicular in Gartner Inc.’s recent report, “Big Data Opportunities, New Answers and New Questions.”
Sicular says that technology is the new business advantage. And that’s a focus of a new suite of upgrades to TIBCO products including Spotfire.
Big data has not historically focused on the operational outcomes, says Matt Quinn, CTO at TIBCO, in a recent press release.
“At TIBCO, our focus is on making it as seamless and as efficient as possible for customers to access all their data – at rest and in motion – giving them the power to quickly use that data to identify and address business problems and opportunities in the moment,” Quinn says.
A really good example of this is the Swiss Federal Railways use of data analytics to accommodate more traffic without expanding its infrastructure.
When the firm couldn’t expand its tracks, it looked at ways to minimize network conflicts. Company executives made this happen through messaging and alerts to drivers, writes Den Howlett for Diginomica.
The alerts run through a big data environment (provided by TIBCO) and business apps predict the traffic speed and movements all over their network. The result is a successful increase in traffic and on-time arrivals within a five-minute window 96% of the time.
See more on this in Howlett’s video blog on how Swiss Railways is “flying by iPad.”
4. Find innovative ways to use big data to grow your brand.
We see a few good examples of how banks are using big data to increase brand awareness, in a new report from Gartner on the use of big data tools in the Asia-Pacific region.
By using analytics in consumer marketing, banks can generate statistics on their customers banking transactions, e.g., average deposit amounts, amount in savings, use of services like loans and credit cards, as well as demographics. They can then enable their customers to play around with the data to see how they compare to their peers.
The end result is to offer a call to action and personalized infographics for customers to share with their social networks. This approach to using unstructured data gives the bank an opportunity to build a relationship with its customers and potential customers in a fun way.
5. Focus on self-service big data analytics.
It’s kind of our party line around here – give insights to the people. It takes the people who ask “What if?” and “How could this affect that?” to make big data projects work.
Working in a self-service model is a good approach for finding insights within teams and business lines. This can tie back to the quick wins.
Small projects can fuel bigger projects and eventually a big data culture. It doesn’t take a data scientist to prove business value when the tools are in the right hands.
There are many more factors that can prove the business value of big data, but getting clear about business goals, empowering your teams and taking advantage of data where and when it happens can bring a big change – through data – to your organization.
Spotfire Blogging Team