Data Quality

data variety / shutterstock

Data variety stands out from the three Vs of big data from the report of the big data survey conducted by NewVantage Partners in 2012. One of the survey results shows companies focusing more on data variety instead of data volume both now and in the next three years.[read more]

Big Data journey / shutterstock

While much around big data remains hype, many companies are in the fledging stages of drawing value from their big data corpus, and given an army of discussions and opinions around the topic, it’s still hard to find a clear roadmap to arrive at the Big Promise.[read more]

Predictive analytics for retail (Image Source)

Here's a true business success story that sheds bright light on the awesomeness of big data. It substantiates the notion that you should store every last iota of data – because you don’t know every pattern [read: opportunity] that might be found or explored.[read more]

Bad data

What keeps IT guys up at night? All that bad data their bosses are using to run the business. By establishing these processes, IT departments can cut the business data deluge to a manageable flow of good information, ensuring that the decision-makers downstream aren’t using two wrongs to make a bad insight.[read more]

business intelligence / shutterstock

By now, most decision-makers have probably heard some success stories regarding business intelligence. But many organizations are struggling to achieve those same results, largely because of impediments like inaccurate reports and a lack of data visibility.[read more]

Big data/shutterstock

Lots of data does not necessarily equate to “Big Data." To my way of thinking, the single most important capability to implement in any large scale data platform that is going to support sophisticated analytics is the ability to quickly construct, high quality random samples.[read more]

analytical hub/shutterstock

Enterprises need to spend time looking forward, rather than just backwards, at historical data. The analytical hub is an important part of making that happen. The analytical hub must be designed properly if it's going to allow data scientists to perform advanced analytics and predictive modeling.[read more]

Big data involves interplay between different data management approaches and business intelligence and operational systems. Consider big data integration as part of your business case and project, because it is essential to gaining the most value from your big data investments.[read more]

Everything in your company runs off of data, but the numbers can’t stand alone—they need to be combined for specific purposes before they help you put food on the table. here are the pitfalls to data consumption and the remedies to turn your data into the “secret ingredient” for your business.[read more]

Big data analytics can help assess the volume of data, while the velocity of data that is potentially in-motion is best handled by what we call operational intelligence. Beyond these, techniques and technology such as predictive analytics and visual discovery facilitate extracting more value from big data. Along with a wide variety of data, these tools help organizations focus on optimizing information assets.[read more]

"I believe that the volume, variety, and velocity aspects of big data, which get so much attention, are secondary. As I have discussed in prior blogs and articles, the most important ‘V’ associated with big data is value. The other ‘V’s’ are only relevant in the presence of value. So what drives that value for big data? Keep reading."[read more]

Among the many Big Data themes we reported on in 2012, one seemed to resonate the most with our readers– all of us with a techie bent have realized that we need more discipline in our use of the term Big Data.[read more]

As important as it is, data modeling has always had a geeky, faintly impractical tinge to some. I’ve seen application development projects proceed with a suboptimal, “good enough”, model. The resulting systems might otherwise be well-architected, but sometimes strange vulnerabilities emerge that track directly to data design flaws.[read more]

Currency trading, like much of the financial services landscape, has been transformed by IT. Today, in an era when currency swings of several cents in a day are common and daily trading volumes are measured in trillions, computers and the networks that connect them are integral components of any dealer’s operation, helping to manage trades, searching for arbitrage opportunities and enabling financial institutions to carefully monitor their risk exposure to their customers.[read more]

Integrated Business Planning (IBP) covers a different approach to planning and budgeting, which is designed to address many of the inherent defects in the way companies plan and budget. Recent benchmark research on integrated business planning illustrates some of these fundamental issues.[read more]