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Using Data Analytics to Develop Measurable Vision Statements
By Fahim Khondaker, Principal Adviser, Data Analytics & Insights at BDO in Australia
Developing a set of interactive visualisations which allow organisations to measure exactly how much each part of an organisation is contributing to its overall vision is one of the easiest ways to generate a Return on Investment in data analytics.
Vision statements are by definition aspirational, yet they do remain the ultimate benchmark for measuring overall performance. In the most basic terms, a vision statement is the culmination of a number of strategies and goals.
The advancements in the field of data analytics, however, make technology readily available which not only allows for the measurement of each goal, but the interaction and relationships which exist between combinations of goals and strategies. This single dashboard view of relative contributions of goals and/or strategies (i.e. cascading KPIs) allows for more informed, evidence based, decision making, and effective allocation of time and effort to areas which need it most.
Data analytics can (and should) be used as a tool to assist businesses to remain focused on their strategies and the key drivers which give them their competitive advantages.
“Understanding the relative contribution that marketing (or any other department) makes to the overall vision of an organisation allows for a more effective allocation of resources”
A good example is marketing expenditure. It is generally accepted that effective marketing is a good leading indicator of future performance, however during difficult times it is an area which is often targeted for cost savings (either specifically or as part of a broader cost reduction exercise). While this may ultimately be an appropriate decision, it would be significantly less risky to make such important decisions following a more holistic evaluation process. Understanding the relative contribution that marketing (or any other department) makes to the overall vision of an organisation allows for a more effective allocation of resources.
One of most common barriers towards implementing an analytics solution that is often cited is the risk of adopting incorrect measures. This risk can be mitigated by developing the measures in consultation with a broad spectrum of stakeholders including senior management and key operational staff. In addition, it is possible to test the likely impact of a chosen set of measures by analysing its impact on historical data and past performance (i.e. would this set of measures have been useful if it were readily available in the past?). It is also useful to adopt an agile approach where measures are continuously monitored and updated if required.
Another barrier that is commonly noted is the lack of actual data required to measure certain strategies and/or goals. It is usually problematic to have organisational strategies and/or goals which cannot be measured. This should not, however, be the reason for not implementing data analytics into a business. The development of the fore mentioned visualisations is an incremental and iterative process. Businesses should start with the data sets that they do have and focus on building upon them. Where relevant data has previously not been recorded by an organisation, it is often useful to run a survey or trial to set a baseline, and then start recording the actual data going forward.