Does the industry make a difference when it comes to analytics?
Are the challenges the same or are they different in banking, construction and retail?
According to Forrester (*), less than 0.5% of data is ever used, but just an increase in data accessibility can result in a significant improvement in profitability. And this remit sits with the Financial Controller. This role has changed significantly with the growth of “big data” or even the collecting, processing and analysis of small amounts of data. It is the accessibility of data and how that is collected and distributed that puts the FD in a truly unique position; giving visibility and insight that empowers decision making and hence why this role is more powerful than ever.
FD’s are no longer just responsible for “balancing the books”, but they are instrumental and strategic when it comes to who has access to what data, how it is used and the resultant decisions that add value to the business. Data is often described as the oil of the digital economy, but what is pivotal is that oil needs refining and so does data, to unlock the true value. This ability to unlock the data is where the power and influence lies.
Data analytics has 4 main pillars;
1. What happened – descriptive analytics
2. Why – diagnostic analytics
3. What could happen – predictive analysis
4. What action to take – prescriptive analysis
A powerful data analytics strategy will work through all four phases in any industry or business; however, the nuances will differ by industry and experience can provide the guidance on what to look out for.
Descriptive analysis mashes up raw data from multiple sources to give valuable insight from the past. For manufacturing this may be a linear analysis of operational costs and output mapped to sales. For retail this can be granular down to size, colour, style or for construction this may include weather, health and safety. But in general, the one thing that cuts across all industries is; “are we making more money than we are spending” or “are we more efficient than before” – this is the bottom line to descriptive analytics.
The why (diagnostic analytics) is where industry knowledge is key as the historical data can be measured against other data to answer the question of why something happened. For instance; Why didn’t someone purchase a product or why was an order not delivered on time? Diagnostic analytics gives in-depth insights into problems.
The next obvious step is what is likely to happen if…. This phase uses the findings of the descriptive and diagnostic analysis to detect clusters, trends or exceptions, and to predict future trends, which makes it a valuable tool for forecasting. Predictive can even use more sophisticated analysis such as machine or AI. This forecasting however comes with a Government Health warning however, that forecasting is an estimate and therefore the accuracy depends upon the quality and stability of the data and the situation, which is, amongst many other factors, industry dependent.
The final stage (prescriptive analytics) is the purpose of the entire exercise to prescribe what action to take. This may be to avoid issues or to take advantage of trends, but it will use advanced tools and technologies and algorithms to decide which path to take.
Throughout all this your data is your biggest asset and to define the right mix of data analytics for your organization, you might ask yourself the following questions:
• Do your current systems give you access to the historical data?
• Can you mash up data from different sources and systems into a data warehouse?
• Do you have the skills internally to access the data and then slice and dice that into meaningful reports?
• Do I have the right technology to extract the data?
• Do the reports I have provide me with the information I need?
• Could my reports be improved?
Answering these questions will help you see where you are on the data and analytics journey and if you need help? The next step might be to design a roadmap towards optimising the technology and the skills you need. Then you will be able to plan the next steps to ensure you are using all four phases of data analytics that will drive the power of decision-making from the insight gained from correct use of the data.
ENDS
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(*) Forrester https://go.forrester.com/analytics/