How ‘good data’ builds stronger business DNA
We all know the score by now. Every company is supposed to realize that it is now a ‘technology company’ at the core.
Whether a business does indeed specialize in IT services and computing, or in fact trades in cake baking and party supplies – the modern enterprise has to underpin itself with connected applications, database services, and analytics.
These are the things that essentially help define any business as an IT-driven business.
This core truism has given rise to the term data-driven business.
Seemingly meaningless as a piece of terminology on its own, the fact that we now describe business effectiveness and potential profitability as a factor of just how competently any individual enterprise is able to harness data is a big thing. It speaks volumes.
So if products and services are now the flesh and bones of any given business model, then data comes to represent the lifeblood of business.
But we can go deeper and take a blood test.
If data is the lifeblood of the contemporary connected business, then we need to be able to look at our ‘blood work’ and examine the very DNA that goes into making this life force flow.
Business-human DNA parallels
Healthy blood along with clean, strong, pure and specialized DNA makes a healthy body.
Equally, we can also say that healthy (business-relevant), clean (de-duplicated), strong (securely locked down), pure (unsullied) and specialized (built for a dedicated use) data will ultimately help make a healthy enterprise.
It is this last ‘pure and specialized’ parallel that offers us some real guidance in terms of the way the data-driven business should now attempt to grow.
Once the enterprise has identified good healthy data, then it can use it to expand its business on an exponential level.
So what is ‘good’ data? It is data that relates to business models and processes that have been defined as quantifiably proven and qualitatively best practice in terms of their application in business operations.
“Good data is data that works. It is data relating to systems, services, applications and individual datasets inside specific workflows that work effectively to produce positive and essentially profitable business outcomes,” said Paul Hardy, Chief Innovation Office at ServiceNow.
“Good data forms the basis of trust in an organization and the more you can reuse it, the stronger and potentially quicker a transformation can happen.”
Good data is reusable data
Hardy further explains that good data is reusable data. That’s the secret ingredient in the secret sauce in the services-centric data-driven business.
We know that good DNA is reusable because it can replicate, and this is very important.
“When enterprise customers harness their data DNA effectively they can start to enjoy incremental improvements as data is re-used across different disciplines, processes and departments. This is apparent when you reuse the same employee data across every department to check, validate and improve the employee experience, in turn, allows for automation and continual service improvement, said ServiceNow’s Hardy.
Good data practice
This theory of ‘good high-value data’ is reverberating around the technology industry. Product Marketing Director EMEA at Sumo Logic Colin Fernandes argues that going forward, enterprises will now need to look at creating a ‘good data practice’ as a working group inside the company premises.
“This means looking at your software application strategy and your business goals together. It involves reverse engineering what your business metrics should be and then turning this into operational analytics that can flag how well you are achieving those goals across IT, software development and business teams,” said Fernandes.
So good data means getting the right information to the right people as they need it.
Chief Data Evangelist at business-intelligence analytics software company ThoughtSpot is Doug Bordonaro. Pointing out that historically, we’ve defined big data with terms like Volume, Velocity, Variety and sometimes Veracity – they’re all important, but they’re all focused on technology.
“The time for experimentation and technical navel-gazing is over. Now a fifth “V” for Value, is gaining primacy as organizations embrace the idea that some datasets are good, often better, than others,” said Bordonaro.
Bordonaro points out that after all, if the actual end users of the information aren’t getting immediate value, it’s not worth doing. This year people are talking much more about the value we can get from data and end-user-oriented solutions are taking over the conversation.
Data value = company market value
Technology and economic analysts now agree that the longer term bearing of this ‘data value and data goodness’ discussion point directly translates to the economic value that any given enterprise will actually build for itself.
“Increasingly the quality of a company’s data has a major impact on the value of that company in the market and, given that we are approaching the tipping point in terms of the application of AI across all areas of the economy, it’s paramount that companies have access to high-quality data,” argues Jonathan Simnett, Director at international technology mergers and acquisitions advisory company, Hampleton Partners.
One further truism surfaces here. Even in the age of intelligent software, deep data analytics and quantum computing powered neural networks, if we put garbage in, we get garbage out. There’s a lot of data out there, but mainly, we just want the good stuff, please.