Business Value for Big Data Part 1

In the last post of this series, I talked about the different types of use cases that are bubbling up for using big data technologies to drive value.  I talked about four buckets that I see these use cases falling in to:

• Faster and more advanced analytics
• Customer 360
• Predictive Analytics
• Optimizing the Plumbing

One of the common blog posts seen over the last few months within the big data community and tech blogs/sites in general is focused on the lack of value that companies seem to be getting from their investments in big data programs.  It is quite common to read one analyst or another writing about the "science projects" that are going on in the market with big data and the adoption of big data technologies being no where near the forecast.  I even read a twitter post from a well known analyst the other day calling for big data companies to focus on "outcomes" versus the technology.

While I do agree with this particular analyst in focusing technology projects on outcomes, I will say that I don't think this is really rocket science or anything new.  Focusing on outcomes should be what every company is doing, whether they are doing the investing or providing the technology.  Without focused outcomes, the project will be doomed from the beginning.

So what are some of those outcomes we should be focusing on for big data projects?

Well, they all come down to the same two big buckets we have seen for many years now:

• Saving Money
• Making Money

Now, one could argue that there are sub categories to these two outcomes, but by and large, these are what business leaders are looking at when investing in projects.

So then, how do the four use cases I laid out in the previous post connect to these two outcomes?

Let's focus on bucket one and two in this post and three and four in our next post.

Let's start with number one.  When looking at the "Faster and more advanced analytics" bucket, value starts to be realized for companies by being able to find patterns that were never uncovered in the past.  As an example, a retailer who was able to optimize their truck routes in logistics more effectively because they had a more advanced way of looking at their data, thus saving huge dollars on fuel costs.  Or a Telco that was able to cross reference data from multiple silos to show broader patterns related to network outages and capacity.   Which directly impacts both customer acquisition and maintenance costs to the tune of multi millions of dollars a year. 

When we think about Customer 360, the associated business value no doubt straddles both saving and making money.  As we talked about in the last post, Customer 360 has been the panacea for marketers and customer service leaders for years.  For marketers, the Customer 360 represents the best opportunity they have at truly understanding their customers wants and needs and then being able to offer products or services that match most closely to those wants and needs.  A great example of this would be insurance companies.  They are one of the "OGs" (originals) in the big data space (along with Telcos), collecting more data in one day that some companies capture in a year on their customers.  Now, by bringing all of this data together in new ways, marketers can offer more granular tiers of car insurance, thus broadening their prospect base.  Or marketers can much more easily help identify customer life events that may trigger offers for new types of insurance to long time customers, thus driving new forms of revenue capture. 

We can not forget though, that the Customer 360 is not only a win for marketers, but also a huge win for customer service leaders.  By giving them and their teams access to the full view of the customer, they are empowered to create a set of processes and experiences for customers that ultimately drive real business value.  Whether it be through providing an authentic customer experience (soft value) or by solving problems faster (hard value) or even starting to be proactive about problems that might be coming (hard value), the Customer 360 drives real value for both customers and companies via the customer service teams.

In our next post, we will tackle bucket three and four, using big data to more effectively predict outcomes and fixing the "plumbing".