Enterprise Data Needs a Good Yogi

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Click to learn more about author Ken Tsai.

When it comes to data complexity, the IT community and yoga community want the same thing – serenity and flexibility.

A recent study, “Data 2020: State of Big Data,” shows that half of the responding IT decision makers believe that data is inaccessible to a wide variety of business stakeholders. With so much data unavailable, it begs the question, “Are enterprise really on an all-digital trajectory? The volume of data created keeps pushing upward, but enterprises aren’t able to leverage it.

Enterprises have always struggled with this. Data Strategies have been all over the map – from what kind of data is needed to what to do with the data once it’s collected and even where to store it, among other things. While the problem isn’t new, some groundbreaking ways to think about data and Data Operations (DataOps) solutions are aiming to solve these long-term data challenges.

With the improvements that are happening at a breakneck pace, it’s important to revisit what some enterprise architects and data specialists view as truths, but in fact are misconceptions or downright myths in the data world of 2018. A central truth, though, is that an enterprise’s greatest needs are core strength, agility and flexibility. These attributes are regularly applied to yoga enthusiasts, and interestingly have remarkable crossover into Data Management. Surprisingly, these three examples of data myths show that yoga and Data Management have more in common than you think.

Myth #1: A Complex Data Landscape Limits Agility

When asked about their enterprise data, nearly three quarters, or 74 percent, of the IT decision makers in the Big Data study said their data landscape is so complex that it limits agility, and 86 percent said they were not getting the most out of their data. That’s an incredible amount of untapped data.

In yoga, there are hundreds of poses, but the typical yoga class only practices a fraction of those available. Many are deemed too challenging for the average student, but an advanced yogi can guide students to expand their practice and reach a complicated handstand or arm balancing pose.

Similarly, newer solutions can orchestrate data across multiple, diverse sources, enriching and refining the data. The modern data orchestration and management solutions take on the role of an expert yoga teacher so that business stakeholders can access data not available to them in the past and apply Machine Learning or Predictive Analytics to it without unnecessary data movement. The solutions support Data Discovery and Data Preparation so that data that was once unattainable is available for decision making.

Myth #2: Data Must be Stored in One Repository with One Format

Enterprises have launched a boatload of projects aimed at moving all data into a single repository with a consistent format. For the most part, these projects haven’t yielded the expected returns. The myth of a single data source aligns closely to the misconception in yoga that only people of a certain size and body type can do yoga. Proof is readily available that all body types can do yoga, and likewise, enterprises can collect and analyze data from multiple sources and formats.

According to the Big Data study, 85% of respondents struggle with data from various locations. They are most challenged by Public and Private Clouds (49%); Data Warehouses (44%); Enterprise Information Management tools (38%); Data Visualization tools (38%); Data Lakes (31%); Data Marts (30%); and Hadoop (26%). Undoubtedly, more data sources will pop up and join this group.

Being prepared for this unknown requires having a flexible and modernized Data Management solution that can adapt easily regardless of data source or format. It must have an open architecture that connects easily to a range of data storage systems and scales easily.

Once connected to the data sources, enterprise architects can create data processing pipelines that accommodate the source and format, accelerating their data-centric projects. When Data Operations have a comprehensive, centralized view of the data landscape, they can capitalize on their data from current—and future—sources. Just as any body type can cycle through a sun salutation, any data format can be primed for a much-needed business app.

Myth #3: Current Data is Not Ready for Predictive Analytics or Machine Learning

Data rarely gets better with age. As soon as it arrives, enterprises have to roll it into the cleansing process. The survey’s results showed that 79% of the IT decision makers say their company’s data needs more than a check up to make it healthy, even though the majority of them (91%) spend considerable time and resources cleaning their data at least once a month:

  • 25% clean data daily;
  • 25% clean data a few times per week;
  • 24% clean data a few times per month;
  • 17% clean data once a month;
  • 7% rarely clean data

The Data Quality is preventing many data projects from moving forward, but that’s not the only factor. Business need a comprehensive view of their data landscape that extends beyond Data Quality. A good yoga practice, for example, is multidimensional and will encompass the body, mind, and spirt. Similarly, enterprises need a new data orchestration and management solution that encompasses governance, security, and policy management. These advanced solutions act as a centralized hub that allow enterprise architects to understand their data across the entire data landscape and not from a single view of Data Quality.

Healthcare Provider Practices Data Yoga

Gustave Roussy, one of Europe’s premier cancer research institutes and treatment centers, is realizing the value of having a yoga-like flexible Data Management framework. Getting data on each patient is crucial in determining the best treatment plan.

Integrating its structured and unstructured patient data onto a new platform, Gustave Roussy now has the ability to integrate different types of information, including:

  • Electronic medical reports, demographics, biology lab data
  • Complex data
  • Genomic data
  • Text analysis
  • Imaging data

With a comprehensive overview of each individual patient’s medical history in a graphical timeline, oncologists and researchers can access information on any level of detail. With easier access and better data query tools, they can make informed decisions at the point of care. The data is both flexible and robust, ensuring that the team isn’t stymied by vast amounts of unusable data or a complex data landscape.

Choosing Data Yoga as a Lifestyle

Gustave Roussy is one of the pioneers in this new approach to data. Rather than believing the myths, the institute created a new data culture that is improving treatments for cancer treatments. This flexible framework was central to tackling a complex data landscape and making it possible for the cancer center to operate more smoothly and become more centered. When centered, a business, a database specialist, or a yoga student can focus on what’s most important and ignore time-stealing distractions.

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