This is one of a series of posts looking at the four key dimensions of Data and Information that must be addressed in a data strategy – reach, richness, agility and assurance.
Data strategy nowadays is dominated by the concept of big data, whatever that means. Every year our notions of bigness are being stretched further. So instead of trying to define big, let me talk about reach.
Firstly, this means reaching into more sources of data. Instead of just collecting data about the immediate transactions, enterprises now expect to have visibility up and down the supply chain, as well as visibility into the world of the customers and end-consumers. Data and information can be obtained from other organizations in your ecosystem, as well as picked up from external sources such as social media. And the technologies for monitoring (telemetrics, internet of things) and surveillance (face recognition, tracking, etc) are getting cheaper, and may be accurate enough for some purposes.
Obviously there are some ethical as well as commercial issues here. I’ll come back to these.
Reach also means reaching more destinations. In a data-driven business, data and information need to get to where they can be useful, both inside the organization and across the ecosystem, to drive capabilities and processes, to support sense-making (also known as situation awareness), policy and decision-making, and intelligent action, as well as organizational learning. These are the elements of what I call organizational intelligence. Self-service (
citizen) data and intelligence tools, available to casual as well as dedicated users, improve reach; and the tool vendors have their own reasons for encouraging this trend.
In many organizations, there is a cultural divide between the specialists in Head Office and the people at the
edge of the organization. If an organization is serious about being customer-centric, it needs to make sure that relevant and up-to-date information and insight reaches those dealing with awkward customers and other immediate business challenges. This is the power-to-the-edge strategy.
Information and insight may also have value outside your organization – for example to your customers and suppliers, or other parties. Organizations may charge for access to this kind of information and insight (direct monetization), may bundle it with other products and services (indirect monetization), or may distribute it freely for the sake of wider ecosystem benefits.
And obviously there will be some data and intelligence that must not be shared, for security or other reasons. Many organizations will adopt a defensive data strategy, protecting all information unless there is a strong reason for sharing; others may adopt a more offensive data strategy, seeking competitive advantage from sharing and monetization except for those items that have been specifically classified as private or confidential.
How are your suppliers and partners thinking about these issues? To what extent are they motivated or obliged to share data with you, or to protect the data that you share with them? I’ve seen examples where organizations lack visibility of their own assets, because they have outsourced the maintenance of these assets to an external company, and the external company fails to provide sufficiently detailed or accurate information. (When implementing your data strategy, make sure your contractual agreements cover your information sharing requirements.)
Data protection introduces further requirements. Under GDPR, data controllers are supposed to inform data subjects how far their personal data will reach, although many of the privacy notices I’ve seen have been so vague and generic that they don’t significantly constrain the data controller’s ability to share personal data. Meanwhile, GDPR Article 28 specifies some of the aspects of data sharing that should be covered in contractual agreements between data controllers and data processors. But compliance with GDPR or other regulations doesn’t fully address ethical concerns about the collection, sharing and use of personal data. So an ethical data strategy should be based on what the organization thinks is fair to data subjects, not merely what it can get away with.
There are various specific issues that may motivate an organization to improve the reach of data as part of its data strategy. For example:
- Critical data belongs to third parties
- Critical business decisions lacking robust data
I know the data is in there, but I can’t get it out.
- Lack of transparency –
I can see the result, but I don’t know how it has been calculated.
- Analytic insight narrowly controlled by a small group of experts – not easily available to general management
Data and/or insight would be worth a lot to our customers, if only we had a way of getting it to them.
In summary, your data strategy needs to explain how you are going to get data and intelligence
- From a wide range of sources
- Into a full range of business processes at all touchpoints
- Delivered to the
edge– where your organization engages with your customers
Next post Richness