Benjamin Henshall, Regional VP and General Manager, ANZ, Denodo, says you don’t have to accept ‘mediocrity’ in how your organization uses data.
It is well understood by CEO’s and their respective leadership teams that it’s not the collection of data that is a primary challenge to improve organisational performance, but rather the continuous delivery of ‘usable data’ into the hands of all their employees, customers, partners and third parties.
For any Executive Leadership Team meeting going on right now around the country, there are questions – and dare I say, frustrating discussions about what to do next, where to reallocate resources, what threats are emerging, where opportunities are for incremental
improvement and how and why the execution of the strategy is or is not meeting its goals or objectives.
“What is the data showing?”
“Do we need more data to validate those assumptions?”
“Can we compare what occurred last period more accurately to this period?”
“What other benchmarks can we use to see what needs improving?”
And on it goes…
We are all acutely aware that in today’s world, executive leadership data combined with experience, technical and organisational knowledge as well as industry and functional expertise, are fundamental to making the right decisions at specific moments in time.
Or, inversely, reducing the risk of focusing on the wrong thing in order to minimise the reallocation of scarce resources on less productive work.
Visualise for example the quarterly Investor Relations slides as might be presented by the CEO and CFO to a company’s analysts and it is clear that the reams of data, graphs, plot and bar charts are either representing a message of success or hiding underperformance.
Notably, ESG is only going to double the amount of graphs and charts their quarterly slide decks require.
Put simply, leaders and company boards have more data available to their decision-making processes than ever before. Cloud, SaaS, customised software, mobile and IoT have made data generation easy, but often sitting across a distributed landscape and in different formats. Because of this, data integration has become complex and troublesome on the whole. As data professionals, we instinctively know that there are rich veins of business value waiting to be unlocked within the mass of data which will help the board and CEO deliver on their strategic and tactical imperatives.
Simply moving data into a centralised repository or the cloud is not the core solution to resolving those questions around data – nor is adding another reporting tool.
Questions about the performance of respective teams will always outstrip the supply of datasets to answer them.
Before losing hope and accepting the mediocrity of how we use data, consider this fundamental paradigm shift that is both conceptually simple and a true game changer in productivity – allowing your organisation to “be brilliant” with the data they already have.
The most valuable asset your organisation’s data holds are not the collection and storage of that data, nor is it in the visualisation or reporting that the data generates – it is in the ‘business logic and business rules’ that are built around the data.
Any company can collect and store data as in the modern world, where data systems have proliferated.
Vendors such as AWS, Azure, Google, Teradata, DataBricks, EMC, IBM, NetApp, Oracle, Cloudera, Hitachi and HP, to name a few, have made significant investments to help organisations collect, store and compute data more efficiently.
Inversely, Microsoft, IBM, SAP, SalesForce, Qlik and Microstrategy have focused their investments on mastering how to visualize that data.
But what is missing here? Asking two simple questions of the organisation can provide that answer.
How was the data curated and what business logic was applied to produce the end result?
Can I drill down and see those business rules to verify it accurately represents my business function?
Most if not all executives have, at some point, been presented with a graph or Excel sheet during a meeting and been skeptical about what is projected in front of them.
While the information may appear in a clear and attractive format, we remain skeptical because we don’t trust the data.
This is the real crux of the issue – the business logic built on the data is the crucial asset for organisational success. For any long-standing company, there are decades of business rules deeply embedded into the data systems that the executive team and their respective teams rely upon every day. Yet trying to adjust, shift, double down and ask different questions of those datasets is a major constraint because those business rules are not transparent, easily accessible or adaptable.
Imagine a world where all that business logic and the data models describing the business have been centralised, made transparent, understandable to business people and eminently reuseable.
Now imagine further that irrespective of which specific application, database, data warehouse or cloud systems are being used – or irrespective of which analytical tool is being used – everybody can now understand what business rules have been applied and track data lineage confidently.
Imagine the control, versatility, trust and agility now available to the business.
The key to making this shift a reality lies in creating a data fabric using a logical approach, which allows you to leave the data wherever it is and connects to it virtually by applying a unified data access layer with common business semantics applied across all datasets.
You can leverage this capability to build custom-tailored data stores to meet your particular needs, again without affecting the underlying data. Users no longer have to spend most of their time collecting and preparing the data, because in a logical data fabric, the available data is ready for you to leverage without the complexities of accessing it.
And here is the paradigm shift – a logical data fabric allows you to reclaim ownership of the data necessary for making informed business decisions that enhance both organisational performance and the business itself.
