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".

How are Companies Using Big Data

So, if we distill Big Data down to this simple concept of just getting more value out of your data, then what kinds of use cases are companies hitting first that is driving value?  Lets use this post to explore some of these use cases and start to make Big Data more tangible for everyone.

Without getting into the deep details of the genesis of the big data space, lets just say that it really all started with a few of the really big consumer focused internet companies in sillicon valley.  Yahoo, Google, Facebook etc.  Because they had so much data on users, it became increasingly more difficult to use this data without new ways of managing it and using it for their customers.  One of the first real areas that applied to big data, was using this new tech called Hadoop to more effectively help run search engines.  But from there, the technology has grown and new use cases for more traditional enterprises have become the focus.

The first broad area of focus in deploying new big data technology has been to make it easier and faster to do data discovery and advanced analytics.  For years people have been reliant on the traditional Business Intelligence tools to help understand what is happening with the data they are gathering.  These tools have been great at what they do for years, but as the amount of data explodes and the time frames for using that data shortens, the new big data ecosystem and technology has become more of the standard way to explore data and look for formerly hidden patterns that are business impacting.  As an example, many manufacturing companies are starting to put all of their production data into a big data system so they can do more advanced analytics on defect detection rates or factory yields.  Or Telcos, who are the original big data companies, are using big data technologies to help determine which areas of their networks are overloaded and how they should plan to spend capital to upgrade them, to keep customers happy.

The second area of focus for use cases has revolved around the infamous Customer 360 that we have been chasing for years.  It seems like every 5 years or so, a new technology or platform hits the market and promises to finally deliver a single view of your customers to the business.  Well, big data is that next technology.  The idea behind something like Hadoop, is that it is a distributed file storage system that lets a company store any kind of data, from any where, in any format, in the same place and bring that data together quickly to gain a single, full view of a customer.  It really becomes the single storage locker for all data being collected about or for a customer and then can be used in multiple ways to add value.  One use case is just being able to deliver a single view of the customer to contact center agents for customer interactions.  Another use case is using this centralized data to more effectively make real time decisions about next best action or offer for customers who are on a website.  Yet another might be using this single consolidated view of a customer to help automate and predict when customers will likely churn.  Picking up on those events that most likely are good predictors of a customer leaving and using them to alert the business before they go.

The third area of use cases can fall into a bucket that is more highly focused on making predictions about what is going to happen in the future.  Again, taking all of the disparate data a company may have, centralizing it into something like Hadoop and then using that centralized data to predict better future outcomes for the company or for customers.  You might have retail organizations that use the mountains of data they have to more effectively plan inventory availability in their stores to ensure customers are happy.  There are also many organizations that are jumping on board with big data to gather data from sensors to help predict outages of machines.  GE is one of the big boys in this space these days, talking about jet engines and wind turbines.  But there are many others that are closer to consumers also using this sensor data to help predict when something is about to go wrong.  Thinking about car manufacturing companies, HVAC service providers and the oil and gas market as a few of the other industries that are using machine data in a variety of ways to help ensure that the minimize down time or outages in their facilities or products.

The fourth and final area I will throw out today in this post is really focused on the plumbing layer of a company and how it can be optimized or overhauled to better serve the business or customers.  I won't go into much detail here, as it can get quite technical fast, but the idea is that there are many data management and storage systems in the market today that are getting long in the tooth.  And with that age, comes incredible expense and risk that many organizations are looking to mitigate.  One example of this would be what is called a "Data Warehouse Offload".  For years, there have been a few companies that have dominated the traditional data warehousing space and as such it has gotten ever more expensive to hold the huge amounts of data that companies are producing.  Many of these companies have begun to off load this data from these expensive, older and less flexible systems onto newer, more agile, more innovation friendly, cheaper systems like Hadoop etc...

So, we will leave it at that today.  These are some of the ways that companies are starting to use big data to bring added value to their companies and customers.  Of course, there are a number of other use cases not listed here, that are adding great value to large enterprises across all industries.  The key is finding the use cases that are going to drive the most value for you and making a plan to see it through.

Next time, we will talk about the value of some of these use cases and who in a company typically should be thinking about these things...

What Really Does Big Data Mean to People?

It seems that I go through these times in life (which I assume most people do) when life hits you up side the head and you feel like you are just a small little boat being tossed around in the middle of a hurricane.  That has what life has been for me in the last 9 months or so, but I am back writing, which is something I truly love to do.

So, lets pick right back up where we left off back in March with some discussion around big data and the value it brings to an organization.  I know some of this may be a bit elementary now, 9 months later, but it still is worth discussion.

Big data to most people is a really nebulous term that really means very little to them.  Almost everyone in the business world has heard or talked about the concepts and what it means for the last many months now, but I am unsure if business folks really understand it.  I would go as far as to say that most still do not.

The way that I look at big data is quite simplistic.  I believe the world of big data is really all about working to get more value out of data, in order to better acquire and serve customers.  Yes, you will have people writing articles talking about how it is game changing, how it is revolutionary, how it is not about big data but small data and the like.  All of this is noise in my mind.  The real focus of any business with data is, how can I get more value out it.

For most, I still think big data is a bit intimidating as it can be incredibly complex.  Within the big data "ecosystem" you have a whole bunch of new or growing concepts that fuse together to help companies derive value for their organization or customers.  There is distributed computing, dataflow, sensors, algorithms, models, machine learning, artificial intelligence etc.... And then there are the names like Hadoop, Hbase, NoSQL or Spark that get thrown around to make things even more confusing.

But lets just keep it simple.  When people talk about Big Data, it is really just a bunch of really interesting concepts and technology that are coming together to help drive value in new ways for companies.  

Again, I know that this is not a game changing post or super insightful for many, but I think keeping the world of big data simple helps many non technical folks begin to wrap their heads around where technology is enabling us to go with our customers.

In the next post, we will take this very simple way of looking at Big Data and apply it to use cases that are beginning to add value for companies.