Data Analytics and Planning

The Importance of Data Analytics in Planning

Planning in a Data Driven Business

Humanity used to think of the world as a mystery place with abstracts, feeling oriented subjects and uncertainty. Now, a significant proportion of us see it as numbers and factors. Today, there are about 2.5 quintillion bytes of data created each day according to Forbes­­[1]. In the other word, it’s more than 320 million bytes generated everyday on average by each person on earth right now. That’s for Internet, Searches, Social media and Communication, that what’s about business alone? Research tells that the average company manage 162.9 TB of data in 2016[2]. Although it’s varied between big and small businesses, it is a huge amount of data to be managed and used properly for any entity.

We have been hearing a lot about how “data is the new oil” but we do not hear much about any fancy vehicles or machines running with that new fuel. It is because we are still in the process of defining the “vehicle” or “machinery” and its engines (Machine Learning, AI Algorithms…). The fact that 60% of global managers and leaders think that half or more of their organization’s data is dark[3] or in the other word, left unused tells a lot. In short, the question is “How can business take advantage of their data reserve and what should they do to enrich the ‘reservoir’?”. There is one clear usage of data is to plan, including both Operation Planning and Financial Planning.

Business shapes data and Data Drives Business

A Hospital manager can say that having more accepted insurance providers will encourage customer to visit the hospital. A Real Estate Salesman can say that growth of income in some residential area will lead to more Real Estate deal closed. But who is there to prove that? How do we really measure the impact? One straight up answer is that it must be proven and measured by historical numbers and related facts.

A traditional planning method involves a lot of assumptions and drivers without being proven by numbers and facts. In the age of information, this might no longer be the case. With the help of data analytics, business’s hypothesis can be drawn and validated with ease.

How Data Analysis can influence planning and how planning can shapes data

There are 4 main types of analytics: Descriptive Analytics, Diagnostic Analytics, Predictive Analytics and Prescriptive Analytics. Out of which, Predictive Analysis of data is the most commonly utilized type of analytics[4]. The first two focus on giving comments on what’s already happened and finding out the root cause while the last two give a forward-looking perspective of what might happen and which actions can be performed to achieve strategic targets. On a typical planning point of view, a business can identify their problem by take a look at the presentation of relevant data (Descriptive), then draw out their hypothesis to rationalize the problem, subsequently prove it by data and number (Diagnostic). Using the results and factor contribution, planner can predict their future performance giving the change in the proven relevant market condition and drivers fluctuation (Predictive), on which, decision makers can also put the action required or sufficient changes to improve upcoming results (Prescriptive).

To put the process into a context, we can take a look at a simple example. CAPEX Investment is a big part of every Hospital Financial Planning, however, it is very difficult to know which types of equipment a hospital should purchase to maintain the equivalent service level while still being cost-effective. By analyzing the popularity and correlation between multiple factors of visitors such as related condition/disease, visited specialty, treatment procedure and equipment required… (Descriptive), we can measure the impact (Diagnostic) and predict the case mix, by which derive number of equipment needed to satisfy respective service level (Predictive). At the end of this, planner should be able to prepare their purchase requisition for future period, both short term and long term (Prescriptive).

There is no fixed sequence of using different types of analytics. Mixing all of these types can help business boost its planning values in multiple areas.

Data Analysis can streamline the planning formulation. By leveraging the help of automation, Data analytics can collect, aggregate and process data seamlessly. On the summary perspective, high level executives can control the centralized plan, track performance, and produce reports while planner can prepare the plan with baseline and proven facts from historical data on the detail perspective. Critically, both of them work on the same set of data ensuring a single version of truth and real-time integration.

Data analysis can identify savings and efficiencies. Data analytics offers decision makers productivity tools to mitigate impact of previous year problem and take respective measure to tackle it. By monitoring planning achievement and real-time performance indicators, prescriptive analytics can help business identify problem as soon as it arrives or even before that, thus, planner can put the right amount of resources on what is important. In the other hand, budget monitoring and forecasting which is more relevant with real-time data can help business trigger immediate action based on market condition.

Data analysis can improve the efficiency of operation and finance planning. Thanks to the help of technology, data collection and aggregation has never been easier. As a consequences, Operation and Financial planning are being pushed together tightly. Financial Planner now use a lot of operational data for their forecast in combination with their existing financial indicators while Operational Planner can optimize results of financial planning to drive operation toward an accurate management expectation.

The starting point

In conclusion, business should be enthusiastic toward a more data centric culture. Although Big Data and Machine Learning have brought the gap between planning and analytics closer than ever, planning is still heavily relied on human’s activities. In estimation, only 33% of the work activities under Optimization and Planning can be automated in 2016 according to McKinsey Global Institute[5], therefore, the story will not only about changing the infrastructure but it is also about equipping management and employees with necessary knowledge to embrace the future of data analytics and planning.

[1] How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read

[2] 2016 Data & Analytics Survey

[3] The State of Dark Data

[4] Data and Analytics - Data-Driven Business Models: A Blueprint for Innovation



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