Driving Efficiency Through Data Analytics
At the end of 2019, many businesses spent time evaluating performance, establishing profit and growth goals and approving budgets. There was also time spent drilling down on results to understand what was working and where changes needed to be made. Depending on the business, it could mean upgrading production equipment, focusing on employee training or revisiting the sales and account management process. This was an easy task for some because of the benchmarking data collected throughout the year. For those with data measuring processes, procedures, inputs and outputs, opportunities are easier to identify. For those with little or no data, it can be difficult to determine where to make changes. Ensuring the company is properly collecting and analyzing data is essential to reducing waste and improving operations. This is why so many companies are investing in data analytics programs. To help clients, prospects and others understand data analytics and its benefits, Clayton McKervey has provided a summary of key points below.
What are Data Analytics?
Data analytics is the process of analyzing data to uncover trends, hidden patterns, and correlations that would otherwise remain buried in the mass of raw data. Companies rely on analytical software tools to collect, compile and analyze data to reveal important insights. This allows management to receive important information for review and decision making. The ability to make decisions based on this information provides a competitive advantage.
Types of Data Analytics
Since the type of data collected can provide information on various processes, it’s important to select the right type to meet the company’s experience, ability, and goals. There are four general types, including:
- Descriptive Analytics – This type uses data to describe what happened over the last month, quarter or other specified period of time. Any data collected is specifically meant to help management discover what happened. Have the number of sales for a new product increased? Is the company selling more in foreign markets?
- Diagnostic Analytics – This type seeks to answer the question about why something happened. This process involves the collection and analysis of several data components across various points in the supply, production or sales process. Seeking to answer questions like did slower delivery times impact sales or how did the latest marketing campaign impact sales, also requires hypothesizing.
- Predictive Analytics – This type focuses on determining what is going to happen in the coming months. This also involves the collection of several data points but requires a more rigorous analysis of multiple variables to understand how they impact a specific outcome. How does changing weather conditions impact sales? If so, how do we manage production to meet sales demand?
- Prescriptive Analytics – This type addresses what a company should do about what they believe is going to happen. For example, if it’s likely a cold winter is coming based on an analysis of 6 weather models, then should the company order more supplies to boost winter production?
Data Analytics Process
Below is a general overview of the analytical process, including;
- Framework – Prior to starting a data analytics program, it’s necessary to bring definition to the process. It’s essential to ensure business goals and objectives are in alignment with the type and amount of data being collected. Remember to spend time defining immediate, short- and long-term goals and review data collection to ensure relevant information is being captured. It’s essential not to collect data simply to have it, which is a common mistake. Rather, identify the insight needed and then collect the necessary data. If the data is not obtainable then be sure to build a process to capture it.
- Measurement – Prior to the commencement of data collection it’s important to select the right software tool to manage the process. There are a number of packages available that fall into several categories such as tagging, analytics, visualization and more. Depending on the framework outlined in the prior step, one tool may be more appropriate than another, to manage the company’s efforts. Broadly speaking, the best solution is one that supports the internal team and allows for the collection and analysis of data from various sources.
- Analysis – Just recording data alone will not provide the actionable steps the process is designed to offer. Most will need to work with a data storyteller to review the data and identify anomalies, trends, relationships, and changes. It’s also important to bring various perspectives on the analysis of the process. Subject matter experts may interpret the data differently due to their business knowledge. Their involvement often brings an additional dimension to the results.
- Action – Once the analysis is complete, management will need to review the information to decide what changes need to be made. For many, it can lead to deeper insights and observations about a process or opportunity, while others may embark upon a new strategic initiative. Since the action step is based on the data collected and analyzed, it’s important to ensure the prior three steps are properly followed. It’s almost impossible to make decisions about what to change if there is inadequate collection or analysis.
The value of a data analytics program can not be understated. The key is to spend time in the set-up and working closely with stakeholders to find the right tools. It can be complicated so many turn to an experienced provider to help guide them through the process. If you have questions about data analytics or need assistance with the program set up or software selection, Clayton McKervey can help. We look forward to speaking with you soon.