Poor Data Quality – Part I: The Consequences
by Heather Smith
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:
Collaborate 12 Oracle Conference Recap
by Roland Hoelscher
I just returned to Germany after spending last week in Las Vegas for the Collaborate 12 conference. arcplan had a booth at the show, billed as the “Technology & Applications Forum for the Oracle Community,” for the first time in 4 years. We’ve noticed trade shows having a bit of a renaissance and this one – with 6,000 attendees – was no exception. With our strong Oracle customer base and our billing as the most widely-used BI front-end to Oracle Essbase, we thought 2012 was the perfect time to return to Collaborate. And after our great experience at KScope 11, the Oracle Developer Tools User Group Conference last year in Long Beach, we want to participate in more conferences where the attendees are the users and creators of BI applications in their enterprises. Collaborate turned out to be a terrific (re)addition to our lineup.
The conference kicked off with a keynote from Oracle CIO & SVP Mark Sunday on “IT at Oracle: The Art of IT Transformation to Enable Business Growth.” His presentation stressed the importance of simplifying IT, which complements arcplan’s vision and our 2012 tagline: It simply works™. With our focus on reducing infrastructure and helping our customers leverage what they already have, this message really resonated with us.
The rest of the session topics reflected Collaborate’s heterogeneous nature as a melting pot of three different user groups (OAUG, IOUG and Quest)…
Mobile BI: Top 5 User Priorities
by Dwight deVera
Mobile business intelligence is a necessity for executives, remote staff and sales people users who need access to business-critical data at all times. Its benefits are numerous and go beyond return on investment. They’re often intangible and hard to describe (and therefore it’s sometimes hard to justify a mobile BI investment). There are many articles from the CIO or CEO’s perspective, but we wanted to hear directly from business users. So we surveyed arcplan clients and compiled a list of priorities for an effective mobile BI solution from the users’ perspective. Their priorities reflect what users around the world expect:
1. Value Beyond ROI
While management insists on concrete ROI for business intelligence expenditures, users are more concerned with the value of BI solutions in their lives. Mobile BI derives its value by delivering at-a-glance views of business-critical information at all hours of the day or night so whether users are traveling, in meetings, or in a different time zone, they can grab their smartphone or tablet PC and get information that helps them take action.
2. Freedom
As BI (literally) moves into the hands of business users, it delivers another important benefit: freedom. Specifically, mobile BI gives users the freedom to view reports as needed, without help from IT and without the limitation of an office setting. Mobile BI users include account managers en route to client sites, supervisors on the plant floor, and store managers who never sit behind a desk – all of whom need data to make decisions at all hours of the day. With mobile BI, different work schedules no longer stand in the way and users become more self-sufficient with the freedom to access information anytime.
3. Bring Your Own Device (BYOD)
The “BYOD phenomenon” refers to users who bring their personal devices into the workplace and connect to the corporate network. It allows users to mix business and personal applications on their own mobile devices rather than carrying separate phones and tablets for work and life. Many mobile BI strategies allow for BYOD so users who prefer Android can use those devices even if the company regularly issues Blackberry phones, for example. In order for this strategy to make sense…
In recent months we’ve explored Collaborative BI as a growing trend and how it’s gaining popularity as an extension of traditional business intelligence solutions. Despite all the hype around this topic, it can be confusing to determine what makes Collaborative BI unique. I’ve seen the terms “Collaborative BI” and “enterprise collaboration” used interchangeably a lot lately, and while both may fall under the categories “Collaborative Decision-Making” or “Knowledge Management,” there are distinctions between the two that are important to understand.
Your data has a ton of stories to tell you – and maybe even a few warnings – if only you would listen. Data talks and today I’m exploring its wishlist: