Data readiness – a precursor to realising the true potential of automation

Shooting for the stars has always been the driving philosophy behind Shared Services, but just how many are capturing new forms of value with Intelligent Automation (IA)? Not quite enough! A recent SSON Analytics workbook –Getting from good to great: The next frontier in Intelligent Automation – suggests that more than half of Shared Services Centers (SSCs) surveyed are far from data-ready to leverage IA solutions in Artificial Intelligence (AI), cognitive solutions, intelligent chatbots, and machine learning. This year, automation has been pinging the radars of Shared Services professionals as the top challenge faced or anticipated. In this article, we look into data readiness - the barrier that organisations have to surmount in order to capture IA’s benefits.

To become data-ready, enterprise data needs to possess three key attributes: Accessibility, faithfulness, and applicability. In most organisations, multiple Enterprise Resource Planning (ERP) and legacy systems capture data in disparate sources. Standalone, function-specific tools and dashboards are also typically used, instead of an integrated data management platform, which can lead to difficulties in consolidating data for use. Data may also be incomplete or stored in non-standardized formats - hardly good candidates for analysis. According to Amazon Research Cambridge & the University of Sheffield, data readiness can be classified into three successive bands - with Band A being the most desirable and band C being the least desirable:

While getting your data house in order, it is key to establish the scaffolding holding the data strategy place: Data architecture, which is defined as ‘a set of rules, policies, standards that specify what data should be collected, how data flows are created, managed and integrated into IT systems and applications (Technopedia, n.d.). It also outlines formal accountability and data governance responsibilities for user groups in the organization, such as, who should be assigned ownership or who can make decisions about data (IBM, 2019). In designing enterprise data architecture, considerations should also be made on how it links to the business process model and whether points of integration can be created across all enterprise applications to facilitate an effective exchange of information.

In today’s highly competitive landscape, businesses are confronting a new normal that is data-driven and intelligence-enabled. Failing to leverage data analytics means passing up the opportunity to turn existing information in your organization into positive business outcomes. The use of cognitive analytics, for example, can sift through unstructured data and deliver prescriptive insights for the business. Data’s potential is limitless. What firms dare to make of it will decide their impact. Gartner estimates that users of modern business intelligence (BI) and analytics platforms that possess augmented data discovery capabilities will grow at twice the rate and deliver twice the business value compared to those who don’t (Gartner, 2017).

For SSCs that have yet to implement a data management strategy, there are only two ways that this can play out: Move or lose.

Find out more about data management and automation in SSON Analytics’ recent VAW: Getting from good to great: The next frontier in Intelligent Automation. Or look up related articles on data management and analytics here!

Sources: 1. Harness the power of analytics and AI to predict what’s next, Avanade (2018). 2. Data Readiness Levels, Amazon Research Cambridge & University of Sheffield (2017), 3. Data architecture definition, Technopedia,https://www.techopedia.com/definition/6730/data-architecture, n.d., 4. Is your data ready for AI? Part 2 – IBM Big Data & Analytics Hub (2019). 5. Augmented Analytics Is the Future of Data and Analytics, Gartner (2017)