data intelligence illustration

Data ecosystem, not just data

Submitted by Stephen Moffitt on
Mon 27/06/2016

I recently had a discussion with a potential client about how they could increase their customer loyalty and improve their cross-selling opportunities. They wanted my advice on how to gather better data from their website and on tools for data analysis. After asking them a couple of questions, I told them my recommendation: “Do nothing unless you are actually creating a data ecosystem.”

Data is not just numbers

Once they had gotten over the shock, I explained that they would be wasting their time and money if that was all they did. It was not enough to bring in a couple of new digital tools and, maybe, hire a data analyst, they had to build an ecosystem that would allow them to actually use the data they were collecting: for actionable insight, for improving operational efficiency or for improving the customer experience. Data is like air. It is essential for life. If, however, I do not have a way to take into my body, transform it into something useful for my body and circulate the transformed products to every cell in my body, then all the air on the planet is not useful.

A transformative data ecosystem

A supportive data ecosystem has a few key characteristics:

  • An appropriate level of data intelligence across the business
  • An enterprise architecture that makes getting and using data simple
  • A culture that promotes relationships instead of control

In order to develop this ecosystem, businesses often have to transform how they relate, to themselves, to their customers and to technology. This is disruptive and needs to be managed sensitively. It requires transformational leadership, who ‘get’ the wider significance of data and are willing to lead by example, particularly when it comes to data intelligence and relationships.

Data intelligence

I have talked about various aspects of data intelligence elsewhere. In summary, data intelligence is being intelligent about data:

  • Educating yourself about data, understanding what it can, and cannot, do
  • Learning to ask the right questions so that data can provide meaningful answers
  • Realizing that data is a part of everything you do, not some separate discipline you can delegate to others

There are, of course, different levels of engagement with data across the business, but everyone, from the members of the board to the summer intern needs to engage with data and educate themselves about it.

An enterprise architecture

Returning to the metaphor of air, in order to be useful, it needs to circulate. Your business’ technology enterprise architecture is how the air of data circulates. As a result, it should be a key strategic priority for the company. Does it support the business objectives in an efficient and effective manner? If not, work needs to be done to develop an environment where data is not trapped in siloed systems or requires risky manual processes to collect and transform.

Data is everyone’s business

Every cell in your body is intimately connected with air, using its products to do what it needs to do. It is the same with data. In order to gain the advantages that data offer, the data must be collected, managed, circulated, analyzed and understood. For that, the whole business is involved. For management, it means giving priority to the tasks related to data, facilitating education and training and communicating the importance of the data ecosystem. The company’s staff needs to see the practical importance of all aspects of the data ecosystem for their jobs and relationships with partners and suppliers need to be designed to support the ecosystem.

It’s not just data

Going back to my conversation with a perspective client, what they needed to understand and include in their project was not just the technology, but what they would need to do to start creating a data ecosystem. This does not mean that they should wait on getting new data analysis tool. It means that the project needs to be looked at within a larger strategy of creating a data ecosystem. This means that data intelligence, enterprise architecture and culture change must all be included in the planning and appropriate work streams developed that ensure the tools can deliver real value for money.