Call to action
Still looking at the conclusion of the Wakefield Research poll on “Data Culture” previously addressed in The Enemy Within, some additional thoughts.
This perceived behavior from C-level executives, creates some perplexity:
They are the ones responsible for the solutions that deliver them the data they need.
They are the ones who endorse the investment made in analytical tools.
They are the ones who have specific requirements for data insights.
They are the ones who depend on data for decision making.
They are the ones that will impact an organizations future.
Why make decisions ignoring data?
If data can’t be trusted, work on data – make it trustable.
Why make decisions trusting on gut feelings?
Experience trains our brains to look beyond the information that is available – And this is great, it’s something only available for the few that can say – Been there, done that.
Also, it’s a capability to be used in exceptional situations – when used as an everyday decision process – leads to making unfounded decisions, to try to repeat the same actions expecting different results.
Work on data
Data trust is addressed removing the causes that lead to data mistrust.
New sales reports are unreliable?
Loan yield vs. risk rating indicators are inaccurate?
AML processes are returning too much false positives?
Customer segmentation is imprecise?
Customer satisfaction scores are sketchy?
All of these are probably data related issues, although sometimes they can be process related, but to build a data driven culture, to be able to leverage all the insights that can be derived from data, these issues must be faced and tackled – To build the trust. To get the best data driven decisions.
Channel data is incomplete?
Inaccurate transaction data?
Too much “dummy data” coming from sources?
Inaccurate customer and contact data?
Incorrect data?
Data is poorly defined?
Too much data?
Incorrect and incompatible formats?
Duplicated data?
Inaccurate data?
Obsolete data?
Missing data?
Compliance issues?
Reconciliation issues?
Obscure and undocumented data transformation?
All and any of the above can be causing the trust issues, and all and any of the above can be solved, making the product of data analysis dependable for decision making.
Start now
Allow me to recover some of the principles I believe essential, not just to recover data trust, but to effectively enable the potential of data driven insights in the organizational decision processes.
Data strategy is business strategy
Use Cases. From here it is possible to identify how data may be used to deliver those priorities and objectives. These will be the use cases for the data strategy. In an early stage, for effectiveness purposes, there should not be more than five use cases, all with clear, achievable objectives and stakeholders that are aware of the importance and impact of data.
Start small, think big
Always aligned with the data strategy start with a small, targeted initiative, where the impact and value of data can be clearly identified and working with a business stakeholder that can passionately and effectively articulate the impacts of data in their business processes and that will be eager to defend the project.
Measure and communicate
Setting up a set of metrics that can be linked to data governance and communicating them across the organization, a success story, that even at a small scale will create the awareness and act as a motor to leverage the replication of that story in other business units.
Business on the driver seat
All the program and initiatives must be driven and oriented by the business units. Data governance is not an IT function, it is a business function, it is the business who better knows what their problems and objectives are. The role of IT in this process is to find the right technology and support the business units in this journey.
Agile mindset
Apply an agile development mindset to all this process, start with a minimum viable solution and iterate, allow that visible results are presented in short time lapses.
Integrate
Data governance is only part of the process of managing the organization’s data assets, it must be integrated with other initiatives, as Master Data Management (MDM) , data quality, data stewardship workflows, data catalog, business glossary and metadata management.
Data Minimalism
All the data being collected and processed in the organization within a specific context, either operational, regulatory, and its collected and analyzed with an end in mind, sustained by a business case and aligned with the business objectives.
Lead by example
Getting the most out of data, no matter in what context is a process that needs buy-in from every level of an organization, and it starts with strong executive sponsorship but also from every other stakeholder in the organization, which need to be aligned and committed.
This means moving from data mistrust and avoidance to action.