Business Intelligence Blog from arcplan
10Aug/120

Invest in Good Data Before Big Data

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Big data is without a doubt 1 of the top 5 BI trends of 2012. The hype around big data has driven many companies to hoard massive amounts of structured and unstructured information in the hope of unearthing useful insight that will help them gain competitive advantage. Admittedly, there is significant value to be extracted from your company’s growing vault of data; however it is data quality – not necessarily quantity – that is your company’s biggest asset. So here are 3 reasons why you should devote more of your IT budget to data quality:

1) Because good data quality sets the stage for sound business decisions.
Sensible business decisions should be based on accurate, timely information coupled with the necessary analysis. Decision-makers need to be equipped with facts in order to plan strategically and stay ahead of the competition – and facts are entirely based on having correct data. Though it’s not as “sexy” as big data, mobile BI, or cloud, data quality should be the foundation of all of these other initiatives.

Admittedly, achieving data quality is tough. Gartner analyst Bill Hostmann says, “Regardless of big data, old data, new data, little data, probably the biggest challenge in BI is data quality.” It crosses department lines (both IT and business users must take responsibility), and processes that have multiple levels of responsibility often suffer from the “everyone and no one is responsible” conundrum. It’s also a complex process that requires laying out common definitions (what is a customer, what are our conventions for company names – Inc. or no Inc. – for example), performing an initial data cleanse, and then keeping things tidy through ongoing data monitoring, ETL, and other technologies.

But ensuring that your data is timely, accurate, consistent, and complete means users will trust the data, and ultimately, that’s the goal of the entire exercise if you see this first reason as the most important. Trusting the data means being able to trust the decisions that are based on the data. Clean up the data you have in place, then you can move on to a strategy that incorporates additional sources of big data.

2) Because you have to.

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12Jun/120

Poor Data Quality – Part 3: Strategies to Combat It

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82% of top performers implement a process for continuous data quality improvement.

–  Gleanster Deep Dive: How Top Performers Improve Data Quality for Better Business Intelligence, January 2011

In recent posts, we’ve explored the consequences of poor data quality and also evaluated who should be responsible for maintaining good data within an organization. We’ve seen that there’s no quick fix for subpar data quality; rather, ensuring superior data requires a well-orchestrated team effort.  A 2011 Gleanster benchmark report revealed that top performing companies understand that maintaining data quality is an ongoing discipline requiring constant attention. Organizations successful in the pursuit of better data have implemented strategies such as these to continuously improve their data:

1. Have a policy in place and take ownership
Organizations may hire a data quality manager as a dedicated resource and the first line of defense against bad data. The data quality manager governs data processes by ensuring that reliable information is loaded into the data warehouse and is responsible for data processes such as migration, manipulation and analysis. Additionally, some BI systems like arcplan allow authorized users to write back data directly to the data source. In this case it is the responsibility of that user to enter accurate information and not corrupt the system with erroneous data.

2. Enforce data quality at the source system
“Garbage in, garbage out” is the phrase to keep in mind. Making particular information mandatory in the source system is one way to go about maintaining data quality…

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29May/120

Poor Data Quality – Part 2: Who Should Be Held Responsible?

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We’re almost half way through the year and by now more than half of us have forgotten or become complacent about our New Year’s resolutions. But it’s never too late to get back on track! The same is true about data quality management; it’s never too late to restore order to your company’s data and combat the consequences of poor data quality. Data quality management should be an ongoing process since bad data affects business intelligence systems and ultimately the decisions based off of BI. It’s a big job and someone has to take responsibility for it. Who should that be?

“Data quality is not solely an IT issue…success depends mostly on involvement from the business side…Business professionals must ‘own’ the data they use.”

–  Gleanster Deep Dive: How Top Performers Improve Data Quality for Better Business Intelligence, January 2011

The knee-jerk reaction to the question of who should be held accountable for maintaining data quality is “the data steward,” “the data quality manager,” or any variation of that role. But who is the data steward? I believe that each organization should have several data stewards and that they should be the content owners or really, the people who most care about data quality. Here are a few examples:

The marketing director who scrubs the CRM system to ensure that lead information is correct often wears the hat of data quality manager. Data quality is important to marketers because good data (email addresses, mailing addresses, and other segmentation fields like revenue and industry) is necessary to avoid fail points in communication and to ensure that the target audience receives your message. With a 2011 Experian QAS research report revealing that 90% of organizations believe as much as 25% of their departmental budgets were wasted during the last year as a result of inaccurate contact data, you can bet that your marketing team has a CRM data clean-up project in the works. Sometimes that means using an appending service to fix bad email addresses and sometimes that means manual research and data entry, but there is true ROI for marketing data quality initiatives.

The account manager who oversees a territory and enters sales and account information in the CRM system is also responsible for data quality…

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14May/121

Poor Data Quality – Part I: The Consequences

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Data Quality - Garbage in, Garbage out?We’ve been thinking a lot about the various ways organizations can improve their existing business intelligence applications. Many of arcplan’s customers have been with us 5-10 or more years and are continuously improving their BI along the way. Some of the initiatives we frequently hear about are related to data quality improvement, but this may be an anomaly. According to Ventana Research’s recent study, less than half of organizations surveyed have taken on some kind of information management initiative, like data quality or data integration improvements, in the last 2 years due to budget restrictions or lack of employees with the right skills.

I’d argue that data quality initiatives should be a “top 5″ priority for organizations in 2012. Why? Because of stories like this: A friend recently told me about a meeting at his company where the regional sales managers were giving their summaries of pipeline opportunities. During one of the updates, a director interjected that he didn’t see the favorable developments mentioned in Salesforce, their CRM system. Based on the information that was present in the system, the director figured that the quarter would be an average one. However, the updates from the sales manager would really swing the potential outcome of the quarter in a positive way. Now I bet that director had to make some decisions that were compromised by the (lack of) current information in the CRM system. He might have started strategizing about how to re-engage with the (assumed) stagnant prospects, started working with marketing on a nurturing campaign, asked the telesales team to reach out…any number of things could have happened based off of the faulty information available to him.

Unfortunately, many organizations have to contend with poor data quality which ultimately results in poor decision-making. After all, decisions are no better than the data on which they’re based. Reliable, relevant, and complete data (as opposed to the incomplete data set available to the director in my example above) supports organizational efficiency and is a cornerstone of sound decision-making. So what are some of the consequences of sub-par data quality?

1) Mistrust. Poor data quality often breeds mistrust among internal departments. I read a great example from 1998 (if you can believe it) that could have been written yesterday:

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