An accurate data analytics process extracts several measurable benefits to businesses, providing information that can potentially boost productivity, improve products and service, retain customers etc. Automating the data analytics process is a great method of receiving quick, actionable outputs from your data.
Leveraging machine learning is an excellent step for maintaining continuous, impactful data analytics. Let’s look at the steps to take in order to improve data analytics through machine learning.
1. Find the right problem.
Do not plan a major project without handling a few small ML projects first. The temptation with ML is to tackle a really big problem, but the right step is to scale it back. Handle the smaller issues within. Choose one that has a lot of data to support it. The chances of success are higher when tackling smaller problems that have a lot of data behind them as opposed to big problems with sporadic data.
2. Develop a business plan and use cases.
An excellent approach is to get people intrigued and excited regarding the possibilities – with a hint of urgency. Implement a business plan and use cases. Notify people that the process will not be easy and will require a substantial commitment of resources as well as time from a supportive business for the initiative to be sustainable. The results will indicate that the time and talent investment was well worth it.
3. Create a strategy aligned with business goals.
The first step in using ML to enhance data analytics is not technical but rather, it is strategic. You should develop an ML strategy that is in accordance with your business goals and KPIs. For instance, if your goal is to reach the top of the Google SERP and your KPI is increasing your web page’s authority, you can develop a strategy for leveraging ML to improve your internal link profile.
4. Ensure you’re ready to leverage ML for analytics.
Machine learning can be incredibly powerful for data analytics, yet ML models work only as good as the data and people that create them. To get the best out of ML for data analytics, the first crucial step is to have a clearly outlined problem that is suited for ML. Make sure that the data used is of high quality – labeled or self-supervised, depending on the problem – to coach the machines and appropriate ML talent in order to get the problem resolved.
5. Identify the data that addresses your questions.
Businesses seeking to incorporate machine learning in their analytics processes must primarily delineate the question of interest, locate data that is fit for the purpose and leverage appropriate technology to deliver credible results rapidly. Within the healthcare sector, there are vast quantities of data available (e.g. from electronic health records of patients) that has tremendous potential for innovations in inpatient care.
6. Automate data gathering systems.
Actionable ML insights can drastically change the path of a business, but they are usually only possible if the data quality supports learning the correct correlations. Businesses must start by investing in the automation of ingestion, normalization and deduplication of heterogeneous data. Off-the-shelf ML models supported by excellent data will almost always have better outcomes than best ML models supported by messy data.
7. Improve the quality of your data.
The learning on good data is accurate. Improving the quality of data is crucial for improving data analytics. High-quality data is extremely important to business, marked with the associated context and a structure that supports automation.
8. Clean up and standardize your data.
A crucial step in planning to integrate ML across a business’s analytics function is to standardize and cleanse data. An often overlooked step, this step is important for ensuring any biases or inaccuracies within the data are not reflected in the results produced by ML models. Clean, standardized data directly provides more valid and reliable outputs.
9. Audit and organize your data.
Audit any available data and organize it in a manner that enables you to consistently gain access, even at scale. For individuals working on ML projects, it’s vital to consider the principle, ‘garbage in, garbage out. The final task of any ML model is to derive patterns and insights from data. So, if the data entered is incorrect, we can expect the ML models to produce flawed interpretations, which can potentially cause considerable damage to the business if it is not identified.
10. Remove data ownership silos.
Data powers machine learning. Leaders must always be open to providing access or enabling data scientists to see and utilize their data. It is important to focus on removing the silos of ownership and enabling the data to work. Therefore, providing transparency and discussing the benefits as well as the ‘how’ is vital, do not simply focus on who currently has the data and where it is.