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Article Blog - Todd March 7, 2014 4 minute read

If Data Analytics are so hot, why all the tension in the enterprise about the topic?

By Todd Johnson, Executive VP and Chief Operating Officer, Saama Technologies

The hype of Big Data is peaking, and a lot has been published about it and what can be accomplished with advanced business analytics.  As a result, many people – regardless of role or function – are looking for ways to get more out of the data available today. We saw in 2013 a growing tension developing inside large enterprises regarding analytics. While there is little debate about the value or even the priority of developing a more powerful use of data science and analytics to drive decisions, the real issue is where the priorities should be from a spend standpoint.  This will only increase in 2014, as more and more people on the business side of the enterprise ask themselves, “what can we learn from the data we have?” and look for ways outside of traditionally IT-driven solutions to reap the benefits of analytics.  The tension is only exacerbated by the rapid rise in interest in analytics based solutions, where IT budgets are not keeping pace with the increasing demands. The two approaches in data analytics that cause this tension can be characterized as Inside Out and Outside In.

Inside Out

Inside Out, in my terminology, is IT driving the development of an infrastructure to support more advanced analytics.  Many large companies have spent the past few years focused on building a data environment that can support its growing need for analytics and big data in a more scalable way.  This story in ComputerWeekly about the three-year project at Unilever is a great example:  Getting the data in place first and then the reporting and analysis tools is a sound strategy that is hard to argue with.

At Saama, we spend a lot of time working on Inside Out projects for our customers.  We frequently do a lot of work building Enterprise Data Warehouses (EDW) and even Data Marts to support specific analytical requirements.  Given the increase in focus on analytics, many companies are diligently working on their infrastructure, even driving further up the maturity curve. A lot of innovation is happening, even at the infrastructure level.  Some of the more interesting projects have us looking at the use of Hadoop to build Data Lakes.  This “refactoring” of the data landscape is a fascinating topic and this paper by Booz Allen Hamilton offers a very good description of the concept. Data Lakes look to be a promising way to build large data repositories that can support structured, unstructured and semi-structured data in a single repository. These and other Inside Out projects provide the foundation for a company’s long-term use of data and their ability for their data and analytics program to scale to the mass of data yet to come. The challenge with these projects is that they are really infrastructure-focused. While this is important, often some elements of the business side of the company will only indirectly benefit.

Outside In

Conversely, Outside In describes projects where the business identifies solutions that utilize analytics to address specific business questions.  The goal is to bundle the infrastructure required to “answer the questions” with the solution, as a packaged offering.  The primary focus is on allowing the business to answer the key business questions without having to sponsor large infrastructure projects.  In fact, Outside In projects really focus on working from the business question and then backing it into the existing infrastructure.

At Saama, we have been investing in Outside In solutions for more than two years.  We have successfully deployed solutions helping customers with questions related to predictive analytics, customer sentiment, employee sentiment, fraud identification and many solutions specific to measuring operational KPIs (key performance indicators).  Quite often Outside In solutions tie to both in-house and external data and need to effectively connect to the enterprise security infrastructure.  As an example, we recently built a patient sentiment solution for a large healthcare provider.  The solution was enabled by utilizing our analytics framework ( to bring the key infrastructure elements to the project, such as data integration, data organizing, data access and analysis, security model, and visualization.  The solution was delivered via the Cloud and only required integration with in-house sources of data.  The result is a very powerful analytics-driven solution with no interruption to the Inside Out projects driven by IT.


Inside Out or Outside In? It’s A Balancing Act

With the Inside Out and Outside In data analytics tension increasing in the enterprise, is there a preferred approach to next generation business intelligence?  No, because it is not about a right or wrong way, it’s a balancing act. Inside Out projects are necessary, but often not sufficient. They are long-term enablers without which the enterprise will never fully leverage the power of the data it collects.  Outside In projects can be very powerful, very practical additions to an enterprise’s approach to analytics.  They can be especially powerful when integrating with outside sources of data.  As you might expect, things never seem to be black and white forever.  Solutions like those from companies such as Good Data ( are starting to blur the lines between Inside Out and Outside In by moving data and analytics infrastructure to the cloud. In many respects they aim to make solid infrastructure projects feel as easy to implement as Outside In point solutions. It is an interesting balancing act that must take into account both long- and short-term needs.  In fact, we are seeing more and more IT organizations starting to look into enabling themselves to become the “analytics solutions bureau” that business needs to meet this these Outside In requirements.  More on this later….

Whether you are an IT organization or line of business function, let us know how we can help!

At Saama, we take pride in helping our customers find that balance!  To learn more, see

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