Organizations today are overwhelmed with the increasing amount of available data, as well as the number of emerging tools and technologies available for data capture, analysis and presentation. As a result, there is an ever-growing demand for data science methodologies to analyze data and provide insights for improving business processes. This demand is no longer limited to large organizations with massive budgets. Rather, organizations of all sizes are recognizing they are immersed in a dynamic, data-driven, highly competitive environment where an effective data strategy is a prerequisite to scalable business growth.
Data analytics provides the actionable intelligence that decision makers need to drive change and optimize business processes. However, building a data science capability is more than just hiring a data scientist – it starts with creating an organizational business culture that is cognizant of the specific data the organization can collect and the potential benefits of gathering such data. To achieve meaningful outcomes and extract value from data, technological and business expertise spanning an array of skills, abilities and perspectives is required, which is why modern data science and advanced analytics requires cross-functional teams.
To successfully build an organization’s data science capability requires a holistic approach that aims to integrate the different, complementary sets of skills of analysts and managers so that employees work as a team to garner value from data. The act of capturing data begins by managing a data source using database management skills. Generally, raw data is not in itself useful because domain and business expertise is often necessary for asking data-related questions, achieving organizational business objectives, developing data-driven strategies, and uncovering data insights.
At Innovizo, our teams include data scientists, software engineers, organizational and strategy consultants, and domain experts, as well as user-experience and visual designers. Working with you, we ask questions, define strategic business objectives, and narrow down the scope of your data requirements to ensuring that data delivers real value and insights. In advising on your data-driven strategy, Innovizo follows the SMART model, where we examine your data platform to help you best identify how to leverage your data assets – whether you’re just getting started, or are looking to get more value out of your data.
The SMART model, initially introduced by Bernard Marr, includes five stages:
S = Start with Strategy
M = Measure Metrics and Data
A = Apply Analytics
R = Report Results
T = Transform your Business
Consistent with the model, we start with strategy, by helping you identify the strategic business objectives of your organization and narrow down the scope of your data requirements so that we can ensure the data delivers real value and insights. Next, we identify and measure metrics and data by examining all the possible types and formats of data that could help you achieve your strategic objectives, and then we apply analytics to find relevant insights. Finally, we report results for smarter decision making and improved business performance.
With this strategy in mind, our team works with you to engineer the solution through disciplined yet agile, phased execution of an organization’s data management strategy. At Innovizo, we help identify the most efficient and cost-effective data science and technology solutions to get value out of your data and translate it into actionable intelligence. Technology can help you collect the data you need and can facilitate analysis of that data, but business acumen is needed so that you are able derive insights from the data that can be quickly and easily translated into action by decision makers.
Andrew Tsintsiruk is Managing Partner and co-founder of Innovizo, a data science and technology consulting company based in Washington DC. Follow him on Twitter @tsintsiruk