Why is Economic Gravity important in becoming an effective data-led business?
Statistically, 80% of business value comes from the top 20% of corporate data, consumed by the broad community of analysts, knowledge workers & decision makers. The business focus is on aggregated data for analytical consumption, but is only valuable alongside the metadata to define the analytical navigation paths, known as the information inventory.
Definition of Economic Gravity
The principle of, and capability for a business to have the effective focus on timely, trusted, common & consistent business definitions (analytical metadata), supported by key aggregated datasets, utilising best in class software automation tools in a cloud architecture, in the hands of empowered and educated knowledge workers, under the guidance of a company wide code of governance.
Business landscape
As 2022 continues with global changes on many fronts, who would have forecast 24 months ago, that we would be riding a global pandemic, a fundamental energy crisis impacting on all businesses & consumers, and the Russia versus Ukraine conflict compounding global raw materials impacting the economic outlook.
One thing we do know, however, is that KPIs, which drive business decision making and company performance are dynamic. That is both in the terms of the data that is expressed in an aggregated way, and the measures & metrics which define and shape that data in a business context.
More so now than ever, it is critical that companies have an accurate and business effective inventory of analytical metrics that can service these constantly changing and volatile trading circumstances. Not being data-led at this pivotal point in our companies could hold grave financial & operational consequences for businesses.
The heritage analytics landscape
We are firmly entrenched in the 21st Century, with all of the advances in technology & automation. However, the demand for information derived from one or many operational data sources, tracks back to the early 1990s with EIS (Executive Information Systems), soon to be superseded by data warehousing and OLAP, as we transitioned towards more distributed computing.
This led to the first generation of business intelligence tools, around the millenium (Cognos, Business Objects etc), and towards 2010, the second generation of business intelligence tools evolved, providing yet greater levels of integration and end user information access. (Qlik, Tableau etc).
Beyond 2010, the advent of cloud computing, and cloud native business intelligence tools have yet further advanced end user access, software product functionality and automation.
Increased automation giving end-user information access through affordable software products has created a greater and more complex information matrix for businesses. They now find themselves supporting between 3 – 8 separate yet interrelated and business imperative knowledge silos, with all the associated costs of training, skills retention, and vendor management, not to mention the multiple versions of truth, to navigate through..
Engaging with the business:
Throughout all of this change, there has been one constant – people – their roles & responsibilities within companies. Irrespective of the functional & technical project scope, and business processes, we need to focus on what business outcomes are important in driving these information demands.
There have been significant changes over this time, including:
- Data volumes – both structured and unstructured
- Corporate metrics – they were and always will be dynamic
- Compute scale – to service corporate data explosion
- Automation – reducing the data friction from source to destination
- Enhanced business user literacy in data and tools
When we take into consideration the heritage analytics investments that are still in operational use, aligned with the key changes above, then accurate analytics data without appropriate analytics definitions means poor decision making..
Analytical metadata is the executive asset register, that is the lifeblood of business value, in today’s data-led businesses. It defines and compiles the business glossary of information & insight through which businesses can determine and decide on the most effective business direction.
So, given this classic business analytics landscape, how do we determine the critical analytical metadata to drive forwards the strategy & performance management?
Through use of automation, effective software tools, and platforms, businesses can reduce the data friction of the journey from data creation to information exploitation. This reduces the time to value, and decision cycles, focused on relevant, appropriate, accurate and actionable information. Actionable information derived from, and aggregated through a corporation, with this orientation of focus, will significantly positively shift the performance management aspect of any business. By being an agent of data, and leading from the executive cockpit, you are driving positive business results.
If we invest the predominant business, operational and technical human skills into the focus on the top 20% of the aggregated business data, then we’ll derive 80% of the meaningful operational, management, and strategic insights to become a leader in business, whatever the strategic mission is.
So back to the question … what is Economic Gravity by definition:
The principle of, and capability for a business to have the effective focus on timely, trusted, common & consistent business definitions (analytical metadata), supported by key aggregated datasets, utilising best in class software automation tools in a cloud architecture, in the hands of empowered and educated knowledge workers, under the guidance of a company wide code of governance.
Where we can add value is at the analytical metadata level, helping businesses, analyse, audit, define, refine, and govern the key management metrics, effectively driving better data-led businesses to enhanced performance.
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