Have you ever wondered what your clients think about your product? Did you ever ponder why they picked your product rather than your competitor’s? Have you ever speculated why one particular software deployment at your company failed while others have gone very well? Over the past several years we’ve been promised that Big Data had the answers to these questions–but many of us are finding this is just not true.
One of the biggest problems with Big Data is that it keeps getting bigger–so it seems as though we are drowning in our own information.
Small Data Provides Context for Big Data
Business consultant and author, Martin Lindstrom suggests that we flip-the-script on Big Data since most of us lack the tools and the time. In his book, Small Data: The Tiny Clues that Uncover Huge Trends, Lindstrom suggests that while we may have a wide-range of data points about our customer’s behavior–what we are missing is the “small data.” He defines small data as the context and connections the human brain makes when examining data points. Lindstrom strongly advocates that “we must not ignore fleeting but instructive glimpses of human behavior in our haste to download and analyze large collections of cold information.”
I see the concept of small data allowing humans to condense volumes of Big Data into manageable and actionable chunks of information. A computer can provide an organization with the information regarding a client’s behavior, but it takes a human to take that information and create a new direction in product development.
Agile and Narratives
As a proponent of agile project management, Lindstrom’s small data thesis strikes a chord with me. Fundamentally, he is building a narrative around user behavior. This narrative in agile parlance is known as a “user story.” A user story describes how a customer or user employs the product–and is written from the perspective of the user. If Lindstrom were to create a user story, I’m theorizing he would gather the mechanical data behind user behavior like clicks, page views, downloads, buying patterns, etc. He would then interview the clients to glean the “whys” behind the patterns of the data. Finally, he would do more wide ranging research in order to plug the hard data, and interview content into cultural context.
Extrapolating Lindstrom’s framework beyond external clients and applying it to internal clients for successful enterprise content management (ECM) deployments could prove to be particularly effective. Analysts firms like Gartner Research laud “contextual relevance” as the future of the ECM marketplace. When I ponder the phrase “contextual relevance,” my take is that this is defined as providing the user with the ability to use content and data to create the narrative (user story) they need to make a client happy or complete a project successfully. Hence, I see the entire purpose of an ECM system as providing a large portion of the narrative as it serves this exact purpose.
ECM Integration with Existing Business Systems Gets You Contextual Nirvana
I do want to mention that it’s almost impossible to deliver on contextual relevance or small data without effectively and efficiently integrating with business systems such as ERP or CRM. Furthermore, these integrations should be direct, dynamic and bidirectional. Integration is important because it provides the connection between data, documents and non-document objects. These linkages serve as a portion of the context to allow users to reduce Big Data into small data.
There’s a lot more to Lindstrom’s work than I’ve discussed here including more detail on how he sleuths out connections which is both entertaining and useful. And if you are interested in more detail about how to write user stories check out my blog post on the lean project process.
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