Here's how you can use data analysis to enhance strategic thinking and decision making in consulting.

As the saying goes, drop by drop fills the ocean- droplets of myriad data points combine to make the ocean choppy or placid! Drawing the right inferences from your data can provide key insights in understanding the pulse of your business. 1. *Correlations* drawn from past data trends (such as Sales against Promotions or Campaigns) guide what may or may not work in future strategies. 2. *Outliers* or Exceptions alert you to focus points, such as a retail outlet facing more stock-outs indicating higher consumer demand for certain products there. 3. *Extrapolation* of data trends help forecasting models, enabling you to sharpen your strategy such as diverting resources from a waning product line to a growing one in your product portfolio.

When it comes to data insights, one of the important concepts is the data -> information -> insight progression. An individual data point in a series often doesn’t yield much, but combining multiple pieces of data can deliver real information about the state. You can’t learn much about revenue without looking at the comparisons to prior periods (growing? shrinking?). It is building on this information that can yield the real insights that unlock learning and action. Combing multiple pieces of information are where the real value comes.

In best case the consultant has a tool to analyse data based on KPI or PPI (process performance indicators) or process flow analysis. Based on the results importants improvements can be found. In the case implementing a new process it is necessary knowing the as-is situation of the envirnonment data, where the new process must be implemented. The data quality is the basis to work on a sustainable strategy.

Data analysis is the activity to find cause-effect relation among data, while strategic thinking is to discover new relation between strategic goal and strategy as measure to achieve strategic goal, and decision making is to evaluate then select strategy among strategic options. Then, data analysis itself will not enhance strategic thinking nor decision making. however data analysis will be used to confirm appropriateness of result to be made after applying strategic thinking or decision making.

Data analysis empowers consultants to pinpoint the root causes of problems, not just the symptoms. By quantifying problems, data allows for informed prioritization and the objective evaluation of solutions. Data-driven models predict the potential impact of solutions, ensuring unbiased decision-making. Tracking post-implementation data confirms effectiveness and enables continuous improvement. Ultimately, data analysis transforms consulting problem-solving from reactive to proactive, helping consultants make smarter, data-backed decisions that deliver lasting results for clients.

When you know the past performance / patterns, it helps you in discovering patterns of the present and future. As a common saying goes "history repeats itself" it is also true in analytics as well. Understanding the data , previous patters helps a lot in problem solving. Half a problem is already solved when data is available for the hypothesis and comparison. When you solve bigger problems , you take a step back and break it into smaller pieces and with the help of data , you solve it piece by piece. In consulting, comparison Matrix plays an important role.

It is necessary to know the hirarchy of data bottom-up from process based data to strategic key performance data. With this hirarchy you can see the connection of processes to the strategy. And you would know which processes must be optimzed to reach a better business.

Someone once told me that the best frameworks are both "meaningful" and "actionable". This has stuck with me and helped me numerous times in my career. A "meaningful" framework helps you learn something about a particular choice. But this is not enough. It has to be an "actionable" space. What do I mean? Let's say you had a framework that identified a market opportunity for your product. And you found super compelling opportunity with one sub-segment. If you have no way to reach that segment it doesn't matter how much they might want to buy your product. You've got to adjust the framework to find the choices that are both yield insight but you can also act on with that insight.

Incorporating data analysis into decision-making frameworks transforms choices from subjective to objective. Data provides hard evidence supporting or challenging assumptions within the framework, reducing reliance on gut feelings. It allows for the simulation of different scenarios, quantifying each option's potential risks and uncertainties. Data also helps define success metrics within the framework, enabling transparent tracking and evaluation of the decision's impact. Finally, post-decision data analysis allows for continuous improvement of the framework itself. Ultimately, integrating data analysis leads to more reliable, impactful choices by grounding decisions firmly in objective insights.

Data analysis is the backbone of Risk Management. Clients are often wary of identifying / accepting the inherent risks in their processes/ functions and historical data analysis often comes to the rescue in such situations. Numbers always have a wider and neutral acceptability. Quantitative analysis highlights that the gaps ( risks) are real and so could be the impact.

Data analysis is vital for strategic thinking and decision-making, especially in risk management. Consultants use historical data and predictive analytics to identify risks, quantify their impacts, and forecast trends accurately. This approach empowers clients to develop mitigation strategies while seizing growth opportunities. By integrating data-driven risk assessments, you offer well-informed recommendations

Craft narratives that contextualize the data, illustrating how it relates to the client's challenges, goals, and aspirations. Dont just rely on data analysis, instead, captivate your audience on an emotional level by weaving a compelling story around the insights gleaned from data analysis. Remember, while data provides the foundation, storytelling adds the human element that truly engages and motivates clients to take action.

Understand the wording and thinking of your customer and present the results in his kind of speach. Don't use conjunctives. And set understandable headlines. No one would like to read pages of text to understand the problem and the solution.

This is a very generic answer I usually read... yes, it will help you refine your strategies over time. However, focusing on harnessing the power of predictive analytics, augmented by the advancements in artificial intelligence (AI), can provide a significant competitive advantage. With AI-driven techniques, consultants can not only anticipate future trends but also uncover nuanced insights and correlations within vast datasets, enabling more proactive and strategic decision-making.

The continuous learing based on a set of indicators that must be implemented for the continuous data analysis. But this must be used from the operative departments and not as a service from the IT department.

Every analytics requirement must start with a clear objective and specific deliverable. Not the general action like “we need to take decision”. 1. What is core activity / business? e. g. manufacturing and selling EV chargers. 2. What is our market or intended market(s). Or is this what we want to decide upon? 3. What expected business volumes are we talking about? Specific numbers! And not small/medium/large. 4. What time period we plan and decide upon? 5 years, 10 years? And not short/medium/long term. 5. How detailed our business plan is? Should we add any important info in it to crystallize analytics framework? Questions like above will help us in scope of analytics which will really help in taking decisions.

7 steps to follow: 1. *Get a clear brief about the issues client is facing* 2. data mining and take out critical data points. Rest for some time and re-start 3. Re check the data extracted for accuracy and re check the data points mentioned earlier. add 5 more points 4. Draw insights and impact of those points 5. Draw a road map with strategy and tactics Rest for a little 6. Re evaluate and prepare final draft Rest 7.Delete the entire plan & start from scratch The plan after deleting the first draft would definitely be better and more accurate All useless pointers would automatically get deleted and you'll get a much more mature plan to share with the client with proper analysis, data insights, and solutions of the problems.