In the movie Margin Call, Jeremy Iron’s character espouses the following insight into success in the financial services industry:
"There are three ways to make a living in this business: be first, be smarter or cheat. Well, I don't cheat.”
That leaves us with being first and smarter. Agility, speed and reaction time to both internal and external forces can mean the difference between capturing a market and making up ground from those who did. According to Gartner, by 2020, information will be used to reinvent, digitalize or eliminate 80% of business processes and products from a decade earlier.
In an information driven economy, the effectiveness of business processes is directly tied to the quality, complements and business-process readiness of the data that drives those processes. Governing those processes also gives the insight into what processes are still needed and those that can be automated via the insights driven from data.
Manage Data for Its Own Value
Look at the balance sheet of any organization and you will find accounting for assets like buildings, trucks, machines and all of the pieces that make up an organization. Each of those assets has a value – but where does data fit into the equation. Just as buildings, machines and trucks are bought and sold today as a matter of course, the ever increasing sharing and monetizing of data as an asset is becoming more prevalent in the market.
Organizations are sitting on vast quantities of data which means they are sitting on what is most often an untapped asset that can be bought, sold and leveraged to further their business. In order to be packaged for sale, a certain level of trust and creditability must be established in the data to ensure adherence to the proper policies and market demands.
As an example, how much is the aggregate trip time of over the road trucking between a distribution center and a city hub worth in the market? I don’t know for sure, but I do see an emerging market for data that could add revenue streams and diversification to the competitive stance of many companies in the near future.
Manage Data for Prediction Based Agility
The recent emergence of machine learning in the public consciousness has introduced the concept of machines and algorithms driving and suggesting decisions to the mainstream business audience. Rather than take humans out of the equation, the trend is to use machines to suggest and influence the thinking of the humans involved in the decision making process. As the reliance on machine learning for the basis of decisions increase, so does the importance of the credibility of the information upon which the data scientists can develop and deploy these algorithms. The management and governance over what data is driven through the process and how that data is sourced and how the results are used will drive the effectiveness of these suggestions.
It is how far these suggestions will go that determine the level of change that will be felt in the business processes and corresponding competitive advantages seen in the market. How much trust will be put into the machine suggestions and how many cycles will be spent checking the machine’s work? Could algorithms be used to get agility to a place where market predictors are analyzed and changes rippled throughout the systems of organizations without a human involved at all?
If possible, then the ability for systems to interface with each other and speak the same language is crucial for any type of automated processing success. Interface ready, transaction ready and business ready data are the foundation on which these types of interactions are built. And it’s this foundation that will help you successfully manage and monetize information to build a competitive advantage.
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