Tableau is very clear and passionate about the company’s mission — to help people see and understand data. Their data visualization technology is second to none, a wonderful front-end tool that makes data come alive.
But when it comes to implementing Tableau, customers often make the mistake of relying on Tableau as a full data manipulation tool without an upstream data management platform or a plan to build one. Tableau has some very handy data manipulation features, but its focus is on front-end visualization, not data management, so these features have a few major constraints.
Data is one of the most critical corporate assets for any organization. What’s more, organizations are generating data at staggering rates, with analysts predicting roughly 50% year-over-year data growth in some industries, with media and entertainment rapidly exceeding these growth estimates due to large amounts of unstructured data. According to a Gartner survey, nearly 75% of organizations are investing or planning to invest in big data in the next two years. Despite these staggering assertions, many organizations are unaware if their data is being properly captured, integrated across lines of business and accurately represented.
Spark Summit East 2016 was held last week in New York City and hosted over 1,300 attendees representing 500+ companies. As Spark’s popularity continues to surge, the unsurprisingly sold out venue delivered on great content through the use cases of Netflix, Bloomberg, Comcast, Capital One, and The Weather Company. Participants looked behind the curtain of Spark 2.0 as Databricks Co-Founder & CTO, Matei Zaharia, shared nuggets of insight on what’s to come and set precedent to a new buzzword, “continuous applications.”
As a result of the Affordable Care Act, healthcare is pivoting from a quantity-based model to a quality-based model and the initial results are encouraging. Indeed, in 2013, overall healthcare expenditures grew at the lowest level since 1960. One major aspect of this revolution is a portfolio of technology solutions that enable Population Health Management (PHM). These solutions aggregate patient data across multiple resources and analyze it into a single, actionable patient record so that care management, collaboration, patient engagement, performance management and demand management can be improved both clinically and financially.
Much like separating the wheat from the chaff during the harvest, selecting the right Master Data Management (MDM) product for your organization can be a long and rigorous process. However, it should also be a celebrated event.
Having the appropriate MDM tool is paramount to proper technical execution of Data Governance and critical to an organization’s data strategy and will bring tremendous value to your organization. Data Governance and MDM function best as a pair, so they need to be snuggly aligned. If you’re looking to avoid ending up with a product that won’t work for your organization, and assuming you already know what you’re looking for in a product, keep these steps in mind so that you can separate the right products from the wrong ones.
Have you ever planned to wake up early in the morning to work out, but instead chose to lie in bed and catch some more sleep? This can happen even after you have committed—mentally, at least—to a new workout regimen. That’s because the hard part isn’t resolving to do something new; it’s adjusting your daily habits and generating enough momentum to carry the changes forward. That requires discipline and drive.
The same challenges apply to data governance initiatives. If you have ever been part of a data governance program that hesitated, backfired or stopped completely in its tracks, you know what I’m talking about. Companies are accruing ever-increasing amounts of data and want to be able to transform all that information into insights the same way you want to get in shape. The first step is data governance, but getting your organization to buy-in to a new program conceptually is the easy part. Taking action and sticking to it can be much more challenging.
Much of the conversation around Big Data has been focused around the technology necessary to collect and store information, but what we should be paying attention to is why data needs to be governed and how to manage it reliably and consistently so that sound decisions can be made…
Case Study: Business Intelligence – Data & Analytics SITUATION In an effort to generate more revenue from its e-commerce site and better understand customer behavior, a large U.S. retailer with 2013 revenues of $25 billion looked for more robust ways…