Evaluate and develop data & analytics capabilities
Overview
Improving organisation data and analytics capabilities while delivering business impact can be a tricky task to balance especially when a strategy is not supported by a data informed roadmap. Aligning business objectives to capability improvements is critical for organisation survival when navigating economic uncertainties.
Taking an “outside in” approach to evaluate core data and analytics capabilities across your organisation or within a business unit is the first step towards building a data driven transformation strategy. The breadth and depth of a review is entirely based upon the level of change the organisation can address within a fixed period.
This post is aimed to inform product owners, strategy and transformation leads, and stakeholders the value of undertaking a capability diagnostic before embarking into a transformation program and how FractalWorks can support you throughout the process.
Introduction
Analytics is becoming key for many businesses’ survival as markets become extremely challenging. Companies like Google, Apple and Amazon place analytics as a core element for all decision making which drives product investment decisions, improved customer experiences, operational efficiencies etc,. Many other industries are following in their direction to great success.
“Forty-seven percent say that data and analytics have significantly or fundamentally changed the nature of competition in their industries in the past three years.”
Catch them if you can: How leaders in data and analytics have pulled ahead — McKinsey Sept, 2019
Pivoting an organisation to be analytically driven can be a significant challenge when the organisation’s data and analytic capabilities are based upon legacy processes and outdated skills. Furthermore, issues such as lack of data accessibility, quality and integrity, poor governance, and compounded lack of modern tools and processing environments hinder the ability to leverage data assets through the application of modern analytics processing.
Addressing these types of issues typically requires a lengthy diagnostic analysis, strategy definition and solution roadmap before any tangible changes is recognised. Therefore it is imperative to incrementally leverage new analytical methods, data, tools, processes and work within the environment constraints to deliver a sustainable transformation and long lasting business impact.
The following four sections discuss the value of a diagnostic process that, in my view, is a key requirement when pivoting into an analytics driven organisation.
Why should you do a capability diagnostic?
Outperforming leaders deliver value through analytics by clear objective setting, leveraging trusted data, applying modern lean development practices and developing collaborative team working. Undertaking a capability diagnostic, across multiple dimensions, provides an evidence based review to inform opportunities to improve upon.
“The majority of companies today adopt a fragmented, siloed approach to analytics tools and data. This approach correlates with diminished business success.”
What Separates Analytical Leaders From Laggards? — MIT Sloan Review Feb 2020
Delivering business success through the use of analytics requires a number of technical and non-technical artefacts and functions to exist. Therefore knowing what, how and when to apply these becomes critical when justifying and aligning investments, and mobilising a workforce to execute the desired changes.
By applying a rigorous capability diagnostic framework issues such as ambiguous objective setting through to unfit legacy processes are discovered. This in turn informs a pragmatic and viable transformation roadmap.
Example issues that cause poor business impact:
- Poorly defined objectives, lack of collaboration between business and implementation teams cause an incomplete or at worst an incorrect solution
- Legacy development practices and manual human driven interventions
- Lack of data confidence due to poor data management processes (i.e. untrusted data, timeliness, incompleteness, quality etc,)
- Multiple team handoffs and onerous approval process
Read full article here https://www.fractalworks.io/post/evaluating-and-developing-data-analytics-capabilities
