At the end of March 2014, people across various industries gathered in Boston for the AnalyticsWeek Big Data & Analytics Unconference. The overarching purpose of the weeklong event, as shared by the organizers, was to share information and capabilities across the Boston big data and analytics community.
A few of my Centric Boston colleagues and I made it a point to attend – some of it at least. Each day of the conference had a different theme and we soaked in the scenes on:
- Day One – Big Data
- Day Three – Insurance & Financial Services
- Day Four – Marketing
Although we attended discussions on different topics, several reoccurring ideas emerged. After comparing notes, here’s our recap of our top four key themes.
1. Realize the Promise.
While companies are recognizing the importance of big data, questions still remain about how best to take action and what kind of impact we can hope to see.
Expectations for big data across industries remain huge. Progress is absolutely being made, including great work from start-ups like DataRobot and Nutonian, along with advancements from established players such as Wayfair and Oracle. But we also heard that in order to get high-impact outcomes and mass proliferation, transformational thinking and broader delivery is needed. Two distinctive analogies about big data adoption were used by keynote speakers:
- From Paul Sonderegger (Oracle) – Where we are now with big data is analogous to where the world stood just as electricity was invented. The invention of electricity was revolutionary, but not every house on the block had it initially and many, many uses for it had not immediately been conceived. So, we’ve done some things with big data, but reality is that while most companies have expectations that they will be using big data over the next few years, most also don’t have a clear vision of exactly how new capabilities will transform their business.
- From Chris Lynch (Atlas Venture) - The world-changing invention of the Internet came first, but to realize sweeping impacts and adoption, the subsequent inventions of the World Wide Web and browsers were required. Is the “invention” of big data enough to drive adoption or will other innovations be required to drive usage and success?
The focus on building momentum is undeniable, with enterprises spending an average of $8M on big data in 2014, according to Louis Columbus. Development efforts will continue to mature and evolve into repeatable, value-based activities. Leaders will emerge!
2. Think in terms of a data stream of consciousness.
Successful companies will utilize their own data, as well as data from third-party providers, to deliver context throughout the endless data collection journey.
On both day one (Big Data) and day three (Insurance & Financial Services), points were made on what constituted a firm’s big data: Successful companies harness their own data, combine it with external data, and use it all to adapt to a world that’s going to change more and more quickly, leaving slower companies in its wake.
When a question was posed on the Financial Services and Insurance panel about utilizing proprietary data versus buying data from outside the firm, the simple answer was that bothare required. Financial Services and Insurance companies aggregate data directly from their customer (demographic, preference-based, transaction history, navigation of firms’ digital media), but they are also big buyers of third-party data and will continue to be. Some examples include census and weather data (especially for Property & Casualty insurers).
All of this data, both structured and unstructured, will help deliver value to the firm and its customers, assuming context and relevance can be established. Think predictive analysis, including highlighting actions that can drive a customer to better personal outcomes.
Speaking of customer value…
3. In service to our fellow man.*
When it comes to defining objectives for the use of big data, customers should come first. Consumer demand for transparency in actions and personalized experiences are only going to increase.
To achieve critical adoption and value, Chris Lynch clearly put forth the notion of simplicity by saying, “We need data science that can be consumed by more than data scientists.” (more on these scientists later). He also highlighted that there are very real privacy concerns and natural resistance to change, so use of customers’ personal data has to deliver value and be transparent for those customers from the start. With that, there is a positive cycle that can be established – give me some data and I’ll provide you with answers to challenges you face in your daily lives (e.g. furniture purchasing decision, investment choices, insurance coverage, etc.).
This was especially clear on day four (Marketing) with the lesson that value comes from analyzing customer behavior and buying patterns across all channels (omni channel) to ensure a personalized shopping experience for every customer. This draws on customer experience principles and can help in customer retention (treat people right!). Of note: measurement on mobile is much more complex than measuring traditional website activity.
If firms want to maximize the impacts they can achieve from big data and analytics, they are best served to think beyond their firms’ needs and desires and instead think in terms of how to create value for their customers (businesses or consumers) with big data.
4, For many, the data scientist is still a mythical creature.
A consensus among experts seems to exist, that the ideal resource has a unique skill set and is difficult to find.
The Data Scientist. Who is this? How do you find them? What can you expect from them? This was a central topic during this Unconference, and has been a topic at other events we’ve attended. Defined as a resource who can combine technology ability (Hadoop, NoSQL, etc.) with statistical aptitude (think quant or actuary), sprinkled with some business expertise or acumen, and rounded it out with the ability to communicate effectively. No wonder why there is a supposed shortage of these experts (McKinsey states that the United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise). Nonetheless, the Unconference speakers tried to answer how to identify and test these resources prior to hiring them. Some sound advice included paying particular attention to what people do as hobbies – do they have big data blogs? Do they opine on big data challenges via networking groups? It was also suggested that specific light problems can be delivered, via the interview process, to test some aspects of problem solving.
Another way to think about this dilemma comes from Jeff Kanel, Centric’s National Business Intelligence Practice Lead. Jeff says that it makes sense to analyze the skill sets needed for specific objectives and consider building a team of complementary resources versus trying to find all the skills in a single resource. In fact, Centric is currently expanding its national analytics practice to fulfill current needs in this space.
So where does that leave us? The takeaways from this conference and from our own experiences point to the fact that getting a handle on big data is important – expectations are emerging both from corporate leaders and from consumers. How companies specifically use the information big data provides, such as building teams, interpreting the data and applying that knowledge to enhance the customer experience, still remains to be seen. Companies that invest the time to figure out such questions now will be leaders of tomorrow.
* This phrase was borrowed from an essay by Albert Einstein – An Ideal of Service to Our Fellow Man