In the current environment, businesses need to move with both speed and precision. To do this, many recognize the need to build a modern data analytics platform and establish a data culture to empower data-driven decision-making across the organization.\n\nBut what does being a modern data-driven organization really mean? What are the hurdles to becoming data-driven? And what role does the cloud play in helping build a modern data analytics platform?\n\nFor answers, we turned to members of the IDG Influencer Network, a community of industry analysts, IT professionals, and journalists. Although their viewpoints varied, as would be expected, there was surprising unanimity in their responses to these questions.\n\n\u201cA true data-driven organization is built around the core values of protecting data as a real asset,\u201d said Scott Schober (@ScottBVS), president and CEO of Berkeley Varitronics Systems Inc. \u201cIt is important to use emerging technologies such as analytical forecasting, machine learning, and artificial intelligence [AI] to separate the valuable needles from the digital data haystacks.\u201d\n\n\u201cThe modern data-driven organization requires guiding people to new ways of working with data on a daily basis by asking questions and self-serving analytical insights,\u201d noted Isaac Sacolick (@nyike), president of StarCIO. \u201cWrangling data in spreadsheets should be replaced by robust data operations, enabled by data prep tools, and improved quality through proactive data governance.\u201d\n\nSacolick\u2019s viewpoint was seconded by Will Kelly (@willkelly), a content and product marketing manager: \u201cBeing a modern data-driven organization is about making corporate data accessible to your business users via self-service tools anytime, anywhere,\u201d he said. \u201cIt means putting in the tools and platforms for data management, governance, and security on the back end. User experience is a paramount requirement to open your data up to business users.\u201d\n\nFor Steve Prentice (@cloudtweaksteve), a technology integration specialist, \u201cbeing data-driven is the yin-yang to intuition and gut feeling. Neither by itself is sufficient to base a company\u2019s future on, nor should one displace the other. A great deal of business success comes from having the right data at the right time but also knowing how to interpret it and what to do next.\u201d\n\nJohn Moore (@ACollaborator), the founder of Trust Enablement, agreed: \u201cI often refer to being data-aware vs. data-driven, but both are far more important than being data-blind,\u201d he said. \u201cBeing data-aware means you leverage a mix of quantitative and qualitative to track your progress towards your business goals.\u201d\n\nIt\u2019s a difficult proposition, Moore freely admits. His advice? \u201cImplement data hygiene, centrally manage the data, create processes to maintain it, educate your team on why it matters and how to do it, and use the data to guide your way,\u201d he said.\n\nIn the past, data-driven usually meant having transactional data from traditional business systems and doing analysis on those transactions to intuit trends and make business decisions, observed technology evangelist Ed Featherston (@efeatherston).\n\n\u201cBut in today\u2019s world, a truly data-driven organization is thinking about what data is needed to drive business decisions and innovations,\u201d he continued. \u201cWith that, applications and technology are planned and designed around those business requirements to ensure that the right data is collected and access is given to the right individuals to be able to perform analysis and make decisions quickly.\u201d\n\nCultural change tops list of hurdles\n\nWhen it comes to the hurdles that organizations confront in becoming data-driven, there was pretty much across-the-board agreement about what leads the list. Featherston spoke for many of the influencers when he said the \u201cbiggest and hardest\u201d hurdle is cultural change.\n\n\u201cBecoming a data-driven organization from a traditional IT structure is a very disruptive process with lots of change,\u201d he continued. \u201cUltimately, technology is easy; culture is hard. This is quickly followed by skill sets in the field of data to help understand what data is needed that provides value, where it needs to go, and who needs to see it. Finally, avoiding falling into the trap of building a data swamp, where you just start collecting data from everywhere, put it in a data lake, and hope and pray analysis of the data will provide value.\u201d\n\n\u201cNot unlike building a culture of cybersecurity, building a data culture within an enterprise can be even more challenging,\u201d agreed Frank Cutitta (@fcutitta), CEO and founder of HealthTech Decisions Lab.\n\n\u201cThe reasons are threefold,\u201d he explained. \u201cFirst, the utility of the data is sometimes questionable. For example, the data might simply confirm what is already known, causing the \u2018ho-hum\u2019 effect from the data users. The second is that \u2014 for many reasons, some generational \u2014 there is a skepticism about using data over intuition. For example, this can be the case with mature doctors as it relates to their using AI. Finally, great pains must be taken to go beyond circulating raw data, or even data visualization, and building a story that can be embraced and socialized by the data constituents. This storytelling and training on the use of the data findings is absolutely critical if there is an expectation of empirical value of the data collection and analysis effort.\u201d\n\n\u201cThe challenge is a cultural one,\u201d said Prentice. \u201cWhen a change is designed and launched based on data, that change will still come up against human nature, which will often put up resistance as a first resort. . . . Management should embrace the idea of becoming data-driven in the same way it seeks to become agile and lean. These can all be buzzwords unless they are respected and understood. They cannot be expected to be a quick fix.\u201d\n\nOf course, cultural change isn\u2019t the only obstacle.\n\n\u201cThe biggest challenge most organizations face in becoming data-driven is in finding and consolidating all of their data assets into one combined entity that can then be analyzed to provide insights,\u201d said Jack Gold (@jckgld), president and principal analyst at J. Gold Associates. \u201cPartial data sets, invalid data, formatting errors, incompatibilities, etc., all make it impossible for companies to achieve the optimum value of being totally data-driven. It also negatively affects the ability to use analytics and\/or AI to provide insights that are accurate.\u201d\n\n\u201cToo many organizations either get overwhelmed with \u2018big data\u2019 and fail to utilize it effectively, or they are so focused on their core product that they only think about data as an afterthought,\u201d added Schober. \u201cBoth approaches are a failure to capture the true value of customer data.\u201d\n\n\u201cBeing data-driven means homing in on the right set of microdata that brings the most immediate results to your organization,\u201d said Sarah Ramsingh (@SarahRamsingh), a Quantum Computing architect. \u201cOne of the biggest problems I see in data collection that is so broad is that companies end up with a \u2018needle in the haystack\u2019 approach. A hurdle to becoming data-driven is not mapping out what questions you want data to answer. This is especially important in workforces with smaller teams. Cloud architects play a huge part in having prewritten models that help your data trees get started.\u201d\n\n\u201cMoving from theory to practical application is not always as easy as it might seem,\u201d said Gene DeLibero (@GeneDeLibero), chief strategy officer at GeekHive.com. \u201cData quality, integration, and inaccurate data are common blockers to becoming data-driven.\u201d\n\nData and analytics have become a competitive differentiator and a primary source of value generation across many organizations. In a recent blog post, data analytics company Tableau discussed how data-driven companies are more resilient and how successful organizations use data to empower more decision-makers right down into the front line.\n\n\u2018The cloud is the technology foundation for data-driven organizations\u2019\n\nAs for the role the cloud plays in helping organizations build a modern data analytics platform, it\u2019s full speed ahead, according to the influencers.\n\n\u201cModern cloud-based data analytics platforms help any organization to process and report the relevant data findings, resulting in enhanced collaboration by offering the decision-makers quick access to business intelligence,\u201d stated Schober.\n\n\u201cThe cloud is the technology foundation for data-driven organizations,\u201d said Kelly. \u201cIt gives you the platform, tools, and security features. If your FinOps house is in order, the cloud offers you a data analytics solution that enables your team to experiment with new tools while staying within budget. Moving your data analytics platform to the cloud gives you better scalability and disaster recovery\/resilience and faster insights.\u201d\n\n\u201cSelf-service analytics and robust data operations fuel the organization\u2019s demand to process larger data sets, iterate on machine learning models, and centralize data visualization,\u201d noted Sacolick. \u201cSo IT needs the cloud infrastructure and data services to enable a scalable, secure, and high-performance data-analytics platform.\u201d\n\n\u201cHaving a cloud solution that is regularly updated with new features and updates is a big positive,\u201d said Alun Rafique, CEO and cofounder of Market Dojo. \u201cHaving the ability to access the system from anywhere at any time is invaluable. This is even more the case in these difficult times, where working remotely is a necessity.\u201dTo learn how to modernize your analytics platform with Tableau on Amazon Web Services, go here.