Understanding the Value Proposition of Looking at Analytics Strategically

Lyndsay Wise's picture
 By | september 26, 2017
in analytics, business analytics, Business Intelligence, dashboard, data governance, Data Management, data metrics, Data Quality, data visibility, information, legacy databases, real-time, self-service analytics, social media, Enterprise Information Management (EIM), Business Analytics, Business Intelligence, Data Governance, Data Quality, Master Data Management, Realtime Data Integration, Self Service
september 26, 2017

We live in a now culture. Everything is about immediacy, with expectations that information not already at our fingertips can be accessed or asked for and delivered with speed. The reality for many businesses, however, is that legacy databases and information siloes have created complex corporate data infrastructures that do not support the social media era of immediate access and collaboration. Low latency and self-service analytics are not always a current reality as organizations struggle to balance their strategic needs with their tactical and operational challenges. Being able to access information when needed in a way that provides business value requires a strategic approach to business intelligence (BI) development, but the need for insights in real time makes it hard to sell the value proposition of a strategic approach to analytics.

Enabling these quick wins and creating an approach to analytics that supports low latency and self-service requires strategic thinking. Many solutions providers promise quick delivery times and analytics access, but these quick successes do not always support more than one business scenario. Once an organization wants to expand, they have to treat their expansion as a whole new project, which can also include the need to re-evaluate data management requirements.

By thinking strategically, businesses can evaluate their broader goals and build up their solutions through quick wins, without having to reinvent the wheel each time new requirements or new dashboards are built. Doing so requires a shift in thinking. Some initial considerations – and these just reach the tip of the iceberg – include:

  • Understand the business value of analytics delivery: It is not enough to identify a set of metrics that should be monitored or to look at the product capabilities required for delivery. Organizations need to understand what the outcome of deployment will be. For instance, monitoring metrics becomes valuable when they can be acted upon. Being able to identify additional opportunities for metrics being met, or to take action when metrics aren’t meeting targets represents the intrinsic value of business intelligence. Simply deploying a solution quickly or being able to interact with a dashboard is not enough to create overall value
  • Look at the audience and their needs: Self-service and collaboration support transparent access to data. Different groups of users will have different business requirements and will interact with technology in a different way. Developing solutions to meet the needs of users supports broader adoption and use. The more people with access to the information they need, the better able they are to make informed decisions and act more proactively
  • Invest in strong data management, governance, and quality: BI and analytics are only valuable if the data is reliable and trusted. Developing a framework to manage information quality requires a strategic approach. Understanding the business implications of bad data or a lack of governance requires looking at the way data visibility affects an organization’s decision-making. By making sure that data is managed effectively, organizations can ensure their analytics access will be aligned to broader business visibility

Strategic thinking when evaluating analytics requires looking more broadly at organizational needs and not focusing on individual deployments. The best way to get there is to look at underlying business challenges and identify the broader business value associated with strong data management practices and holistic analytics access.