5 Levels of Search Maturity
May 10, 2016
Identifying social patterns to enterprise search
In 2014, Jon Doctor and I sat down to quantify to clients why their search
just can’t “work like Google.com.” We started by organizing the patterns detected in our clients’ implementations
and sales pursuits. The output of this was the refined concept of organizational search maturity.
It helps us explains “why can’t it just be like Google”.
Like the CMM or the Sandler Sales process , organizational search maturity is an opinionated set of guidelines that help provide guidance on the current stage and steps to realistically improve the trajectory and the value of what enterprise search technology can provide an organization. It also provides an action plan for moving customers from one level to the next in their search process.
We all know that an estimated 80% or more of the world’s content is ‘unstructured’. As with any personal improvement program, understanding and improving your organization’s search maturity is a multi step process that involves a combination of reflection and action. The first step is to understand, and ultimately, accept your organizational search maturity.
Level 0 - Most Companies
Described as:
- Users can run search for documents you know exist;
- Executives and management don’t know about or don’t care about the problem;
- Organization has bought a practical search mechanism which might be considered for the tasks of an organizational group;
- We most often hear ”not a strategic objective” at this level;
At this level, for most companies, the value of search does not gather a lot of attention. There is a justification hurdle here as the analytics and the data can be pretty
Level 1 - Identified Information Access Problems
Described as:
- Having identified unmanaged aggregation; and
- Having deployed a federated search tool.
Some companies or departments have taken an initiative (customer support, sales enablement, employee on boarding). They have begun to fold all of their knowledge into a system.
The challenge is that they get highly relative (to the keyword) content but useless for the objective. The may have purchased a tool or begun to build a search system.
Level 2 - Classification & discovery
Described as:
- Bringing structure to the unstructured;
- Visual cues that transfer context more universally than plain text; and
- If you know what to ask for, you can get answers.
- Begun to review analytics
Content analyzation leads to analytics that dive deep into a customer’s decision making process. Pivoting your enterprise knowledge on predefined data points allows for effective decision making. This also aids the user to be able to communicate what they are looking for in the cases of “I don’t know how to ask for help”.
Level 3 - Answer Engine / Content analytics
Described as:
- Knowledge mining;
- Search Meets Business Intelligence; and
- Data in context is better, answer the question.
- Analytic Driven Approaches
Tools like Siri provide a glimpse to a broad consumer case, but these can be easily adapted to specific use cases with far greater success. Computers can detect patterns better than humans and lead to improvements in product develop, fraud analytics and research. What is good for the very complex is good for things that are seemingly less, think Google Now. Case deflection and knowledge assist can provide direction when asked.
Level 4 - Intelligent assist
Described as:
- Predictive search and knowledge management;
- Search is the application; and
- Tell me the question I should be asking.
- Tell me the question I should be asking? Tools like Google Now analyze consumer trends and provide feedback, but what if this had more meaning? A computer can tell you, based on common trends, “what am I missing?” Directing your users before being asked can help them understand the questions they aren’t aware they should be asking. Providing users the data to make informed decisions proactively.
At MC+A, we’ve been doing this for 15 years. We can help you get to Level 4. Contact us.