This post highlights some of the key points to keep in mind when you are starting on data analytics journey. You may want to check a related post to assess where does your organization stand in terms of maturity of analytics practice – Analytics maturity model for assessing analytics practice.
In the post sighted above, the analytics maturity model defines three different levels of maturity which are as following:
At whichever level you are in terms of maturity of your analytics practice, it may be good idea to understand the following points to come up with data analytics projects. Believe that a lot of prior work is required to be done before starting on the analytics projects. The fact that a large volume of data is available is not enough to assure success with data analytics projects. The picture below represents the kind of homework which needs to be done prior to starting on analytics projects.
Here are the key points to keep in mind before starting on analytics projects, in particular, and analytics initiatives at large.
- Understand that analytics can be applied at all levels in the organization including operational (tactical projects) and management levels (strategic projects).
- Identify decision points at all levels in the organization related to different business functions. Most of the time, the decisions related to different business functions are made based on some data and lot of intuition. The idea is to blend or mix up data-driven analytics projects with intuition to make better decisions. One must remember that using the data analytics with intuition could bring competitive advantage for any business in terms of stakeholders ability to make more informed decisions. The following are some of the examples of decision points at both strategic and operational level:
- Cost reduction or cost savings (This mostly becomes key focus for analytically challenged organization – one who are starting on analytics journey). Business stakeholders look for the business functions where they are spending a heaven and want the expenditure to be reduced resulting in cost savings. One can look for such opportunities to increase the cost savings by working on those projects related to business functions where lot of spend is done.
- Forecasting including cash forecasting, demand forecasting, inventory forecasting
- Identifying target markets, new suppliers etc
- Annual budget allocation
- Deciding on new products / services
- Determining pricing models
- Plan & prioritise analytics projects keeping in mind short-term tactical gains vs long-term strategic benefits. For example, coming up with key visualization reports which helps gain actionable insights in relation to cost savings etc could be short-term / ongoing gains. However, putting up data strategy including big data strategy, data governance, data-driven products / solutions etc should be taken up as initiatives pertaining to long-term strategic benefits. Once the projects have been identified, the key is to prioritize these projects to realize maximum business value in ongoing manner. While prioritizing the projects, do keep in mind the positive business impacts the projects will have. The idea is to strike a balance in between getting ROI and laying down foundation for long-term strategic analytics.
- Start analytics projects with question-first-data-second (top-down) approach rather than other way around. Begin with end in mind. The questions are about business problems. More often than not, it is seen that the analytics team starts with looking at all the data that they have and coining the analytics projects including predictive analytics projects which can solve one or more business problems. And, this is where most of the analytics project fail.
- Make sure that you have appropriate metrics laid out upfront to assess the success of analytics projects. In this relation, I would recommend you may want to read through one of my other posts on success metrics for analytics projects – Different Success / Evaluation Metrics for AI / ML Products
- While planning for analytics projects, identify all project teams and stakeholders including individuals that you may need to work with during execution of analytics projects. The following can be a set of different teams who could get involved with analytics projects:
- Teams which will be involved in analytics project execution; This will be team including analytics project execution team, business function teams (primarily, business analyst from different business teams)
- Team (end users) which will use the analytics deliverables
- Team that are responsible for data (Product / business teams)
- Team which are responsible for data quality, data governance
- Team which will be responsible for measuring / reviewing the success
- Team which will gather the feedback on analytics projects outcomes
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