Deriving Actual Value from Data
This is a four-part series.
Part one: Deriving actual value from data
Part two: Being “data-driven” (but not in a good way)
Part three: Aligning People, Process, and Data
Part four: Data Synergy For Business and IT
Deriving Actual Value from Data
Written by Jeff Gleason
According to the Harvard Business Review, less than 50% of structured data (like rows in a spreadsheet) is used in making decisions — and less than 1% of unstructured data (like sensor data from a machine) is analyzed or used at all.
More than 70% of employees have access to data they should not, and 80% of analysts' time is spent simply discovering and preparing data.
A bit sobering, isn’t it?
Marcavel wants to help you understand why companies that proclaim to be “data-driven” are often contrarian. In fact, most companies invest more in storing data than they do actually deriving value from data.
A lack of evolution
The most painful point of the Harvard Business Review article is how 80% of analysts’ time is spent discovering or preparing data. It paints a bleak picture reminiscent of ancient nomadic hunter-gatherer tribes searching for resources to survive.
That 80% of the hunting-gathering effort suggests that in a 261 workday calendar year, "valuable" analysis on data does not happen until sometime in October of that year.
In fact, while we live in an age where technology can capture and place unimaginable amounts of data at our fingertips, study after study demonstrates that companies simply lack an understanding of how to derive actual value from their collected data.
Why the disconnect?
Without the ability to effectively use data and derive value from it, businesses can expect to experience problems directly associated with this failure. But what causes this?
The ability to articulate a business strategy without definition. For example, a company might decide they need to reduce supply chain costs by $X, but the inability to clearly define or articulate what data they need - and how they need to use it - to achieve their reduction goal becomes an almost insurmountable obstacle.
Mismatched goals between business and IT. IT departments face budgets that force them to do more but spend less while the business’s demands often force IT into frequent strategy and technology changes based on trends, opportunities or initiatives.
The inability to articulate a strategy, and the lack of cohesion between strategies for IT and the business, means that data juggling occurs through the replication and movement of data from one technology platform to another.
Data strategy as a solution
What makes a data strategy different from a business strategy or a technical strategy, is that it predominantly focuses on the education and enablement of business analysts and users to better define and articulate their data needs to the technical organizations supporting them.
Simply put, a sound data strategy bridges the gap between a company's business strategy and the technical approaches that IT uses to support those business initiatives.
One can think of a data strategy as the Rosetta Stone for linking the business and IT groups together. A data strategy offers the "translation language" of the business goals to the technical deployment strategies used by IT groups, simplifying both business and IT processes while delivering higher value from data.
A well defined and executed data strategy positively impacts both the business and IT goals - supporting both lower costs and increased value to the company.
Lacking a sound data strategy, business analysts will continue to wander the "data desert" in search of answers, wasting precious time and money.
If your team is ready to quit wasting months of the year chasing data and start utilizing it, send our team a message to inquire about our ecosystem review and digital transformation services.