Archive for the ‘Analysights’ Category

“Big Data” Success Starts With Good Data Governance

May 19, 2014

(This post appeared on our successor blog, The Analysights Data Mine, on Friday, May 9, 2014). 

As data continues to proliferate unabated in organizations, coming in faster and from more sources each day, decision makers find themselves perplexed.  Decision makers struggle with several questions: How much data do we have? How fast is it coming in? Where is it coming from? What form does it take? How reliable is it? Is it correct? How long will it be useful?  And this is before they even decide what they can and will do with the data!

Before a company can leverage big data successfully, it must decide upon its objectives and balance that against the data it has, regulations for the use of that data, and the information needs of all its functional areas. And it must assess the risks both to the security of the data and to the company’s viability.  That is, the company must establish effective data governance.

What is Data Governance?

Data governance is a young and still evolving system of practices designed to help organizations ensure that their data is managed properly and in the best interest of the organization and its stakeholders.  Data governance is an organization’s process for handling data by leveraging its data infrastructure, the quality and management of its data, its policies for using its data, its business process needs, and its risk management needs.  An illustration of data governance is shown below:

governance

Why Data Governance?

Data has many uses; comes in many different forms; takes up a lot of space; can be siloed, subject to certain regulations, off-limits to some parties but free and unlimited to others; and must be validated and safeguarded.  But just as much, data governance ensures that the business is using its data toward solving its defined business problems.

The explosion of regulations, such as Sarbanes-Oxley, Basel I, Basel II, Dodd-Frank, HIPAA, and a series of other rules regarding data privacy and security are making the role of data governance all the more important.

Moreover, data comes in many different forms. Companies get sales data from the field, or from a store location; they get information about their employees from job applications. Data of this nature is often structured.  Companies also get data from their web logs, from social media such as Facebook and Twitter; they also get data in the form of images, text, and so forth; these data are unstructured, but must be managed regardless.  Through data governance, the company can decide what data to store and whether it has the infrastructure in place to store it.

The 6 Vs of Big Data

Many people aware of big data are familiar with its proverbial “3 Vs” – Volume, Variety, and Velocity.  But Kevin Normandeau, in a post for Inside Big Data, suggests that three more Vs pose even greater issues: Veracity (cleanliness of the data), Validity (correctness and accuracy of the data), and Volatility (how long the data remains valid and should be stored).  These additional Vs make data governance an even greater necessity.

What Does Effective Data Governance Look Like?

Effective data governance begins with designation of an owner for the governance effort – an individual or team who will be held accountable.

The person or team owning the data governance function must be able to communicate with all department heads to understand the data they have access to, what they use it for, where they store it, and what they need it for.  They must also be adroit in their ability to work with third party vendors and external customers of their data.

The data governance team must understand both internal policies and external regulations governing the use of data and what specific data is subject to specific regulations and/or policies.

The data governance team must also assess the value of the data the company collects; estimate the risks involved if a company makes decisions based on invalid or incomplete data, or if the data infrastructure fails, or is hacked; and design systems to minimize these risks.

The team must then be able to draft, document, implement, and enforce its governance processes once data has been inventoried and matched to its relevant constraints, and the team develops its processes for data collection and storage.  The team must then be able to train employees of the organization in the proper use and collection of the data, so that they know what they can and cannot do.

Without effective data governance, companies will find themselves vulnerable to hackers, fines, or other business interruptions; they will be less efficient as inaccurate data will lead to rework and inadequate data will lead to slower, less effective decision making; and they will be less profitable as lost data or incomplete data will often cause them to miss opportunities or take incorrect actions due to decisions on such data.  Good data governance will ensure that companies get the most out of their data.

 

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Big Data, Big Bucks

May 6, 2014

(This post appeared last week on our successor blog, the Analysights Data Mine)

In their 1996 bestselling book, The Millionaire Next Door, Thomas J. Stanley and William D. Danko constructed profiles of the typical American millionaire.  One common characteristic the authors observed was that these millionaires “chose the right occupation.”  When Stanley and Danko wrote Millionaire, I doubt many of their research subjects were data analysts, predictive modelers, data scientists, or other “Big Data” professionals; but if they were to write a new edition today, I’ll bet there would be a lot more on the list.  “Big Data” jobs seem to be “the right occupation” today.

In a recent interview with the Wall Street Journal, veteran analytics recruiter Linda Burtch of Burtch Works predicted that job candidates with little familiarity with “Big Data” will face a “permanent pink slip,” while observing that analytics professionals earn a median base salary of $90,000 per year. Ms. Burtch distinguishes between “analytics” professionals (who typically deal with structured data sets) and “data scientists” (who typically work with large, unstructured data sets), when classifying income levels.  Data scientists, Burtch Works found, make a median base salary of $120,000.

Even more impressive is the median base salaries of entry level professionals, those with three years’ experience or less: $65,000 for analytics professionals and $80,000 for data scientists.  At nine or more years’ experience, the median base salaries rise to $115,000 and $150,000, respectively.

Much of the reason for the hefty salaries is that companies don’t often understand what skill sets they need.  Ms. Burtch mentions this in her comments to the Wall Street Journal, and I indicated as much in a previous blog post.  Add to that the fact that needed skill sets are also highly specialized and relatively few professionals have such skills, or a large pool of them.  Because of the scarcity, candidates can command such high salaries.

For companies, this suggests that in order to get the most value out of a “Big Data” hire, it must first decide the typical projects it will expect the candidate to perform, and then base the required skill set and years of experience accordingly.  Then the company can budget the salary it is willing to pay.  This will ensure that the company isn’t hiring someone with 10 years’ experience in data analytics and paying that person $120,000 per year just to pull data for mailing lists, when it should have hired someone out of college for about one-third of that.

For candidates, the breadth of skill sets employers seek in “Big Data” professionals suggests they can maximize their salaries by continuing to broaden their skills and experience within the data realm.  For example, someone with years of SAS programming and SQL experience may branch out to other programming tools, such as R and Python. Or, such a professional may expand his or her skill set by developing proficiency in data visualization tools such as Tableau of QLIKVIEW.

Working in “Big Data” may not make someone “the millionaire next door,” but it may bring him or her pretty close.

 

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Check out the Analysights Data Mine

March 26, 2014

As many of you have seen, the last post on Insight Central was in April 2011. I just wanted to take this moment and let you know that I have christened a brand new blog this morning, the Analysights Data Mine, which discusses trends and developments in the field of “Big Data.” If you love Insight Central, you’ll also love the Data Mine. Insight Central will remain up for your enjoyment, although I have no further plans to post on it. Thanks for your visits and loyalty to Insight Central; I look forward to seeing you again on the Data Mine.

Forecast Friday Topic: Evaluation of Forecasts

April 14, 2011

(Last in the series)

We have finally come to the end of our almost year-long Forecast Friday journey. During this period, we have discussed various forecasting methods, including regression analysis, exponential smoothing, moving average methods, the basics of both ARIMA and logistic regression models. We also discussed qualitative, or judgmental, forecasting methods; we discussed how to diagnose your regression models for violations such as multicollinearity, autocorrelation, heteroscedasticity, and specification bias; and we discussed a series of other topics in forecasting, like the identification problem, leading economic indicators, calendar effects in forecasting, and the combination of forecasts. Now, we move on to the last part of the forecasting process: evaluating forecasts.

How well does your forecast model perform? That question should be the crux of your evaluation. This criterion relates to your company’s bottom line. You need to consider the costs to your company of forecasting too high and of forecasting too low. If you own a toy store and your sales forecasts for some stock-keeping units (SKUs) is too high, you risk marking down those items on clearance. On the other hand, if your forecast is too low, you risk running out of stock. Which type of mistake is more costly to your company? How much error in each direction can you tolerate, affordably? These are questions you must consider.

Your models are useless if you don’t track how well they perform. Any time you generate a forecast, your model will not only give you a point forecast, but also a prediction interval associated with a given level of confidence. The point forecast is the midpoint of that prediction interval. Each time you generate a forecast, record the actual results. Did actuals fall within the prediction interval? If so, how close to the point forecast did they fall? If not, how far off were you?

As you keep track forecasts vs. actuals over time, determine how often your actuals fall within our outside your prediction intervals, and how close to the point forecast they are. If your forecasts are frequently far from your point estimate, especially near the upper or lower bounds of your prediction interval, that’s likely a sign that your model needs to be reworked. Indeed, model performance degrades over time. Technological advances, societal changes, changes in tastes, styles, and preferences, and random events can promote forecast error, because forecasting models are based on past data and assume that the future will continue to resemble the past.

Forecasting is as much an art as it is a science. And I hasten to add that the ability to forecast is like a muscle – you need to exercise it in order to strengthen it. Forecasts are never consistently perfect, but they can be frequently excellent. Don’t look to become a forecasting “guru.” It doesn’t last. Allow yourself to learn new things from every forecasting process you go through and each forecast evaluation you perform. And if you do that, becoming a great forecaster is in your forecast! And I can’t think of a better note on which to end the Forecast Friday series.

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Tell us what you thought of the Forecast Friday series!

We’ve been on a long road with Forecast Friday. I began the series last year because I believed that forecasting is an art that every business entity, or marketing, finance, production (etc.) professional could use to go far. Many of you have been tuning in to Forecast Friday each Thursday, so I would appreciate your honest feedback. Please leave comments. Let me know the topic(s) you found most helpful or useful. What could I have done better? What topic(s) should I have covered? Please don’t hold back. The purpose of Insight Central and Forecast Friday is to help you use analytics to advance your business and/or career.

Forecast Friday Topic: Does Combining Forecasts Work?

March 31, 2011

(Forty-second in a series)

Last week, we discussed three approaches to combining forecasts: a simple average, assigning weights inversely proportional to sum of squared error, and regression-based weights. We combine forecasts in order to incorporate the best features of each forecasting method used and to minimize the errors of each. But does combining forecasts work in practice? The literature over the years suggests that it does. Newbold and Bos (1994) summarize the research on the combination of forecasts below:

  1. Regardless of the forecasts combined or individual forecasting methods used in the composite, the combined forecast performs quite well, and is often superior to the individual forecasts;
  2. The simple average approach to combining forecasts performs very well;
  3. The weights inversely proportional to SSE generally performs better than regression-based weights, unless there’s just a small number of forecasts to be combined and some forecasts are much superior to others. In situations like those, regression-based combining methods tend to work better than simple averages and weights inversely proportional to SSE, or the worst forecasts are excluded from the composite.

Why does the combination of forecasts work? Makridakis, Wheelwright, and Hyndman (1998) provide four reasons. Generally, many forecasts can’t measure the very thing they desire. For example, it’s very hard to measure demand for a product or service, so companies measure billings, orders, etc., as proxies for demand. Because the use of proxies can introduce bias in forecasts, the combination of forecasts can reduce the impact of these biases. Secondly, errors in forecasting are inevitable, and some forecasts have errors that are much greater than others. Combining the forecasts can smooth out the forecast error. Moreover, time series can have patterns or relationships that are unstable or frequently changing. By combining forecasts, we can reduce the errors brought on by random events in forecasting. Finally, most forecasting models minimize the forecast errors for one-period ahead. Forecasts are often necessary for several periods ahead; yet the further into the future we aim to predict, the less accurate our forecasts. Combining forecasts helps to minimize the error of forecasts several periods ahead.

Whenever and wherever possible, organizations should try to generate forecasts via many different approaches and then derive a composite forecast. Different approaches touch on different functions within the organization and increase the representativeness of the real world factors under which it operates. When those factors are accounted for in the composite forecast, accurate predictions frequently emerge.

Next Forecast Friday Topic: Evaluating Forecasts – Part I

Next week, we will begin the first of two-part discussion on the evaluation of forecasts. Once we generate forecasts, we must evaluate them periodically. Model performance degrades over time and we must see how our models are performing and tweak or alter them, or remodel all together.

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