Four steps to overcoming cultural resistance to advanced MI & reporting capabilities
When discussing the value of MI and reporting solutions with clients, we often encounter a common concern: cultural resistance. The benefits of an advanced MI and reporting capability are rarely disputed, but implementing it involves winning over and training up large consumer groups who have grown used to MI and reports being handed to them. We hear that Excel, for all its limitations, is a tested and trusted companion and PowerPoint, despite the manual effort, is a familiar friend.
Even once advanced MI and reporting tools have been implemented, it is not guaranteed that their potential will be realised as the cultural resistance is often strongest at the top of the organisation. The following example can be found littered across the financial services sector: a monthly PowerPoint report that had been taking 10-15 days to prepare underwent months of rebuilding in Power BI. Incremental time savings continued to be found as the myriad of data sources were automated, but the one part of the process which is apparently sacrosanct is the 2 days spent screenshotting the Power BI dashboard back into PowerPoint for the final output. There is just one consumer of this report, albeit a very senior one.
With the volume of reports growing and MI requirements getting ever more complex – especially as businesses continue to wake up to the value and potential of their data – the case to move away from error-prone and time-consuming manual processes and towards ultra-efficient automated self-service gets stronger. Even so, it is still a long journey to implementing an MI and reporting tool and maximising its functionality for a scalable, consistent and efficient solution.
There are four key steps to delivering an MI and reporting solution, with each layer adding complexity, but each also offering incremental time savings and a step towards unlocking the full potential of advanced MI and reporting solutions.
Let’s assume the starting point for the current capability is a relatively low (though not at all uncommon) level of maturity, characterised by: Excel is the most common tool, data is exported from systems and requires manipulation into something meaningful, and data is translated into PowerPoint or other template for reporting and governance packs. From this point, a typical implementation approach might have the following building blocks:
- Report building: Reports previously in PowerPoint (or equivalent) are transferred over to a data visualisation tool (e.g. Qlik, Tableau or Power BI) to create dynamic and visually appealing dashboards. Data is still manually exported from source systems, formatted in Excel and imported to the data visualisation tool, but from there dashboards are updated as periodically as required without any additional work. Having said that, consumers may request the dashboard graphics are added to PowerPoint templates as screenshots. Whilst this is a time consuming and low value-add activity, there will be examples where this is a valid and necessary end game (e.g. regulatory reporting). Data visualisation modules that schedule automated PowerPoint or PDF reports – such as NPrinting by QlikSense, Tableau’s Reporting tool or Power BI Report Server – still makes the exercise a valuable time-saver.
- Strategic data sourcing: The visualisation tool is hooked up to source systems to receive either periodic or real-time feeds. Although there are pre-sets for linking to a number of common data sources both internal (e.g. SharePoint, SQL) and external (e.g. the Web), it is likely that taking automated feeds from source systems will require work from the system BAs to format extracts in the right way for consumption, so start building those relationships early. Depending on the complexity and size of the data sourcing requirements, it may be necessary to implement a data consumption layer, such as Cloud based solutions provided by Google, AWS and Azure amongst others.
- Consumer self-service: Consumers can now self-serve reports, they are accessing the tool directly in governance forums and, where requested and appropriate, regulators are able to review reports. In order to cultivate this culture, it will be necessary to train and grant access to all end users. By this point (if not earlier), consumers will want mechanisms to provide reassurance in some key areas:
- Transparent data sources, calculation logic and intended use of metrics will inform consumers of exactly what they are seeing and how it should be used;
- Defined accountabilities (e.g., data input, data modelling, report design) and access to a rapid support network will be valued in case data quality is queried or there’s a technical fault;
- A forum to air feedback, incremental enhancements, and requests for new reports will foster strong stakeholder relationships.
Mobile access may well be an appealing feature for today’s digitally hyperconnected but physically dispersed workforce but note that this often requires an add on to licenses that comes with a cost.
- Strategic insight: Now that the dashboards are built, data is automatically sourced and consumers are self-serving, the MI and reporting team will have capacity to drive genuine insight for the organisation. It is at this point that a data visualisation tool can pivot from delivering MI and reporting to offering value-add business intelligence. Their built in AI capability is becoming increasingly sophisticated but extracting insight will still need a resource mix that is data literate, experienced with the tool and knowledgeable in the subject matter that can be blended with a “keep the lights on” and incremental enhancements reporting team.
Increasing quality and control of an organisation’s reporting output, whilst reducing the resource overhead, is a daunting challenge. Reverting to tried and tested methods can be seen as the path of least resistance, but these are not scalable solutions at a time when the volume of reports and requests for MI is only set to rise.
Implementing a scalable MI and reporting operating model, with an effective data visualisation tool at the heart of it, is fundamental to managing the workload. But just as important will be cultivating a business culture where consumers play their part in self-serving MI and interacting with dashboard reports. The less digitally native the audience is, the tougher the challenge, but a step-change approach will layer on the benefits without overwhelming the consumer.