In a recent BNET article, Michael Hess, shared a philosophy for making business as simple as possible. Called Occam’s Razor, this philosophy boils down to making things as simple as possible to increase action and productivity across the organization. Hess has three questions worth considering when you are implementing or evaluating data analytics or business intelligence projects across the organization.
1) Are you using information to the best of your ability?
According to Hess, “many organizations bury themselves in reports, graphs, charts and other data.” That’s why it’s important to think about the type of data that you actually need when making decisions.
In a recent Information Management article on data quality for the next decade, this subject was approached. William McKnight asserts that data needed for these applications or “enterprise data” lacks standardization, which will “allow us to project how business will be conducted in the next decade.”
McKnight says that data must have “true quality across the enterprise.” This falls right in line with Hess’ question of whether we are using the voluminous influx of data to our advantage.
McKnight’s hypothesis for data quality is based on three principles:
1. Information volume is exploding – both in accumulation of inside data and through channels and third-party sources.
2. Today’s business world is real-time, meaning that opportunities have to be realized early, often and accurately.
3. Information is a key business asset no matter what business you’re in; information management is a strong competitive differentiator today.
As timeliness in decision-making becomes more imperative, organizations will need to be more nimble in not only data capture but also in the analysis and the conversion of that data to a decisive action, if they are to stay competitive.
2) Is your company wrapped in red tape?
This fits right in with what McKnight says about the data standard organizations need to achieve. Red tape such as who owns what data and what procedures are required to access that data are barriers to success in creating a system of “true data quality across the organization.”
In order to achieve true enterprise data, McKnight suggest that cooperation across the organization concerning data quality is the one of the keys to success in achieving an enterprise data standard.
3) Do you ask for too much information, especially information that you don’t really need or won’t really use?
McKnight’s other key to success is the actual entry of data – not just how it’s entered, but what is entered. He says we are often hasty to build the system and focus on “optimizing the throughput of transactions,” but we don’t pay enough attention to the quality of the data we’re passing through the system.
What to do? Make data entry and the type of data going into your systems at the entry point a priority because according to McKnight, “the value of data entry to the initial receiving system is estimated to be only 10 percent of its overall value in downstream systems and the enterprise overall.”
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Spotfire Blogging Team