Analytics can be invaluable when it comes to making decisions, evaluating performance and determining whether a particular project has been a success. But many are underutilizing the power of analytics.
Here’s 7 steps to help you make the most of this valuable resource:
Step 1: Establish goals and expectations
Analytics usually involves collaboration between various groups. Agreed upon goals and expectations should make clear which tasks will be taken on by artificial intelligence (AI) and which tasks will continue under human guidance. Once those parameters are established, automation can take over to handle basic tasks and efficiently deliver quick results.
Step 2: Save time and money
Data science projects take about two to three months to finish. The longer they take, the more they cost. Collecting and sorting data is a basic task that is time-consuming, so if you're able to save time, you're able to save money and yield a better ROI.
Although you can't trust everything to AI, a significant amount of data can be collected and organized by computers. Data preparation, data validation, data ingestion, and data maintenance are all tasks that can take up a lot of time and effort until they become automated. Faster results mean faster decision-making.
Step 3: Analyze data effectively
Automating data will significantly help to analyze Big Data in ways that will lead to improved decision-making. Effective and quick delivery of high quality analysis will allow you to make informed decisions and assess problems in real-time.
You can also continuously measure the performance and use of automated processes through specific metrics. This helps ensure total effectiveness by checking that the objectives laid out in step one are being met. These metrics also come in handy for different projects or expansion.
Step 4: Increase productivity and efficiency
Existing infrastructures and systems are not able to handle the demands of data science, which can create problems when it comes to processing data and delivering insights in a timely manner.
Skilled data scientists are in short supply, and you don't need to waste them on tedious, unimaginative tasks that lay the groundwork for more complex work when they could be focusing on the high-impact work that demands their best skills and true expertise.
Productivity immediately goes up when AI takes on the busywork of data analysis. According to Adobe, 78% of organizations implementing AI increase operational efficiency by more than 10%. It's a savvy way to cut down on workload while increasing productivity at the same time.
Step 5: Turn analytics into results
Don't use all your time, energy, and resources on collecting data without getting the results you need. Accurate and fast delivery of your insights is necessary for overall success.
Automating your data processes reduces errors while cutting the time it takes to finish complete analysis. Streamlined data analysis makes for instant insights that will have a significant impact on your decision-making and, ultimately, a greater ROI.
Step 6: Select the right tools
To truly unlock the power of your data through AI, you need the right tools to make it happen. Choose programs that are reliable and well-supported. Make sure they align with your overall goal and are capable of meeting your needs now and in the future.
Once you’ve made a selection, test them out to be sure they don't end up setting you back. The right tool should carry out your needs quickly and efficiently without causing trouble or draining resources.
Step 7: Evaluate
Once you implement the process of automating data, remember to monitor and evaluate to enhance your results. To gain maximum ROI, you want to ensure the systems in place are producing results faster and smarter than before. Look for ways in which you can upgrade or make adjustments for maximum benefit. Pay special attention to changes in results or your goals and adjust accordingly.
The future of analytics
Although AI is still developing, there have been considerable leaps forwards and can provide organizations with a number of benefits. For data scientists, AI can assist with many tasks while reducing the workload and the time it takes to complete a project. The Mckinsey Global Institute estimates that automation alone could raise global productivity from 0.8% - 1.4% annually.
Additionally, non-data scientists can also easily access the results of data-driven tasks. Getting a chance to understand and work with data suddenly becomes accessible to a wider group of people.
These technologies continue to evolve and become more capable of many facets of data science. Companies who have implemented AI have already experienced many of the benefits outlined here. Much of the manual work involved in data science is on the way to becoming fully automated and creating a new standard for the world of data analysis.