In this post, you will learn about the top 10 data analytics strategies which will help you create successful data products. These strategies will be helpful in case you are setting up a data analytics practice or center of excellence (COE). As an AI / Machine Learning / Data Science stakeholders, it will be important to understand these strategies in order to deliver analytics solution which creates business value having positive business impact.
Here are the top 10 data analytics strategies:
The most important aspect of data analytics strategy is to identify the business problems which need to be addressed. Many a times, data analytics teams start with spending lot of time (3-6 months) in figuring out what can be extracted from the data. And, end of the day, not much get extracted from data in terms of business value.
On the other hand, when starting with business problems, it helps in staying focused in attaining the business value by defining KPIs, setting up data collection strategy, setting up data team, executing and deploying and finally measuring business value.
The business problems can be found out with the help of key business stakeholders. It would be helpful to understand their VGIs and KPIs to identify top 2-3 business problems which can be solved using analytics based solution.
Once the top 2-3 problems are identified, the next step will be to determine all the business / product and engineering teams will need to be involved in order to carve out the analytics solution. The primary focus will be to understand some of the following:
The most important part of data analytics strategies is to have a measurement plan in form of Key Performance Indicators (KPIs). The idea is to identify key metrics which will be used to measure your analytics goals.
Let’s take an example of a procurement business problem – Shorten the procurement process cycle. The solution to this problem may represent some of the following:
Here is what the Business KPIs may look like, which can be used to measure the effectiveness of analytics solution:
Once the KPIs get identified, the next step can be used to identify / come up with related analytics solutions including traditional reporting / predictive analytics solution which can help achieve business KPIs. The following would need to be done in relation with planning analytics solution:
Gathering the right team with the following skillset will be key to achieving success in any data analytics project:
Once the problems and solutions are determined / defined and the data analytics team set up, the next important step will be to determine what kind of data is needed and all kinds of data sources which will be used to extract the data. The data sources could be internal or external to the organization. This is a continuous process.
In order to do efficient data collection, one would require to consider different set of tools and frameworks which can be used to gather the data set. The technologies related to big data, data lake takes prominence while thinking through data collection strategy.
Once the analytics solution is defined and teams have been set up and data collection strategies in place, the next steps are some of the following:
One of the important aspects of taking the analytics solution to the end users is how we deploy these solutions in the production. Given that the analytics solutions can be served to multiple customers (internal or external), the solutions might require to be multi-tenant. In addition, different products may choose to adopt the solutions at different point in time. The release cycle of analytics solution would need to be planned appropriately. The versioning of analytics solution is also important.
After the analytics solution is moved into the production, the next important step is to make sure that the solution reaches to the end users and that the end users adopt the solution. This requires appropriate training to end users, regular follow-ups in order to make that the end users are onboarded with the solution.
There will need to be strategies to measure some of the following in continuous manner and identify areas of improvement:
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