What if we could finally solve the complex data equation?
Nowadays, thanks to the growth of digital transformation and investment, every business is now in the data business. Even before the pandemic hit, many organizations had already started realising the value of data and recognising their dependence on not just data, but good quality and accurate data, for business success. A CEO at an American bank once revealed to me that his 103-year-old bank still did everything on spreadsheets, even though it was trying to become the kind of highly profitable, digital-first bank that anticipates financial needs and empowers its clients with frictionless experiences. Many businesses recognised that they needed to become a data company when the pandemic hit; the need to transform digitally became not just important, but also urgent. However, even though companies want and need to become data-driven, they are not all that successful at doing it yet - something our recent survey revealed. Nearly 40 percent of the business leaders in the UK make the majority of their decisions without data. This result may not be surprising as the same survey reveals that the first challenge UK business leaders have with data is ensuring data quality (53 percent). An inefficient piecemeal approach  For decades, managing and using data for analysis was focused on the mechanics: collecting data, cleaning it, storing it and cataloguing it. However, the preoccupation with the mechanics of the data management process created some enormous challenges, including there being no connection between the people who prepare data and those who make the decisions or assess the state of the business and a piecemeal approach to managing data, which creates silos within companies. People and systems on the front lines need to be able to easily validate that the data fuelling day-to-day business is reliable and risk-free, so that companies can gain a deeper understanding of the data they have to assist them in driving better business outcomes. To get this reliable data, it is no longer enough to simply have solutions in place that are highly effective at moving and storing more corporate data. Focusing on getting, moving, and storing more data means that companies are drowning in data, which is why the data management market itself is estimated to be worth about $130 billion. While capturing and storing data used to be a problem, it was never the end game. Companies would collect as much data as possible and figure out how to use it later, but then later never came. So, those very same companies had in essence created a digital landfill of corporate information, with no way to easily and securely sort through the volume to find the data they need to make data-driven decisions. Too many companies still havent addressed some of the most basic components of the data equation, like knowing what data they have, where it is, who is using it and, perhaps critically, they have zero ways to measure the datas health. Getting a pulse on corporate data health  Visibility and clarity into datas reliability, risk and opportunity to provide value to the business is key. Ironically enough, data is the most intangible business asset, so every organization should use data management solutions that provide the knowledge needed to make smarter, more agile decisions, while avoiding potential risks. To get a measurable and quantifiable view into their data, every business no what size it is, should check its data health. Our aim is for data health solutions in the future to help create a universal set of metrics to evaluate the health of corporate data and establish it as an indicator of a company or business strength. Rather than treating data as simple, concrete units: cells on a spreadsheet, fields in a database  passive digital objects waiting for an analyst, we need to understand thats no longer a sufficient model as data is complex and constantly changing, depending on how it is being updated, its use context and who has added it. Each business has its own unique requirements, regulations, and risk tolerance; so just like human health, data health would be different for companies of every age, life stage, and maturity level. Talends initial framework imagines three areas for companies to focus on as they begin the journey to establish data health. Preventative measures, where organizations pre-emptively identify and resolve data challenges, effective treatments to systematically improving data reliability and reducing risk, and a supportive culture establishing an organizational discipline around data care and maintenance. The combination of technologies and cultural practices that form this proactive monitoring system to ensure a business achieves data well-being will be unique to every organization, but, critically, the standards applied will be universal. A vision for a better future  In the not-too-distant future, businesses will look back and wonder how they ever functioned without a quantifiable way to measure the reliability, risk and return on data. One savvy company that has already invested in digital transformation to achieve success and ensure data health is Vyaire Medical, a global respiratory care company. Its data infrastructure was a patchwork of inefficient structures, including 12 enterprise planning systems, meaning that employees had to collect data from wherever they could, meaning decision makers often received conflicting data depending on its source. There were so many design elements, top to bottom, that were never built to scale to the numbers we needed to scale to, said Ed Rybicki, Vyaires global chief information officer. So we really needed to rethink the whole thing.  This meant making some key infrastructure decisions  moving to a cloud infrastructure rather than remaining on-premises, building a centralized data repository that anyone in the company could access, and instituting data quality standards. In short, they made the call to ensure healthy data to anyone who needed it for analysis and business decision-making. In 2020, COVID emerged, so there was more demand for Vyaires ventilators than ever before. We had to replicate a highly customized manufacturing process for this line of ventilators, said Rybicki. It was probably 20 times beyond what had ever been done before, all in six or seven months. We were able to scale up these old systemsand help save lives.  Because Vyaire made deliberate investments in the health of its data, the company had complete clarity into its entire operation from factory floor to the boardroom. They were now able to answer the call of a lifetime. No one should ever have to make decisions on information they cant find, see, or understand. The ultimate goal of creating a data health practice is not just to establish confidence, but total visibility into your data, and therefore and a real quantification of value. We believe that once you establish your companys baselines for data health, you wont be able to imagine life without it. Christal Bemont CEO, Talend