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3 Key Questions for Data Analytics Projects

Occams Razor 3 Key Questions for Data Analytics ProjectsIn 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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Amanda Brandon
Spotfire Blogging Team

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Bill Cabiro

At some point, serious focus on improving the quality and structure of the data will have to take place, especially when considering that only 12% of all data is to some extent structured.

In large and mid size companies the technology is light-years ahead of the quality and structure of the data. It’s common to see state of the art Business Intelligence software that cannot deliver strategic direction or analysis until armies of analysts download the data into spreadsheets, manipulate, fix and structure the data manually, for hours or days at a time.

In my experience, even after years and millions of dollars spent in BI deployment, Marketing and Sales organizations feel like they are drowning in an ocean of data and yet thirsty for the Strategic Knowledge they need to grow the business.

Every time someone needs a quick answer about market share, profitability or growth of a particular market, product line or customer, it takes weeks to go through the process of running the right queries, exporting them to Microsoft Excel, manually cleansing data errors, adding look-ups from other data sources, and finally creating pivot tables to find the right answers.

Even worse, they have to go through the entire process over and over again every time they need a progress update, either the following week or at month-end, quarter-end or year-end for each one of the business units and markets they serve.

I can’t help but ask, Is this a good use of marketing and sales people’s time? Shouldn’t they be investing that time doing market research or in front of customers finding opportunities to grow the business?

In addition, a large portion of team meetings is spent arguing whose data is correct instead of focusing on the critical issues. The numbers generated by Finance do not agree with the analysis performed by Marketing or the explanations provided by Sales. They need a single version of the truth, but different folks run different queries, made different data cleansing assumptions and customized their spreadsheets based on different metrics.

On the other hand, when data is properly structured by Marketing & Sales, before being loaded into the applications, and BI tools are configured in a manner intuitive to the users, the software now provides the answers the strategic commercial folks need to grow the business.

The teams finally share a single version of the truth. Now analyses run by Finance are in agreement with those run by Marketing and Sales, right from the BI application since manipulation of the data in spreadsheets is no longer necessary.

Configuring the BI / analytics software to perform strategic and competitive analysis on-the-fly gives the company a competitive edge that results in increased market share, revenue and profit. This is the most effective way to increase the return on the BI investment, normally so difficult to justify when BI supports just back office functions and not strategic and profitable growth.

Regards, Bill

Lindsey Niedzielski

Great post. I agree that there is too much red tape in data management. We have a community for IM professionals and have bookmarked this post for our users. Look forward to reading your work in the future.


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