A Crystal Ball for Business

A Look Inside Your Business

Companies harness the power of AI and data analytics to predict customer behavior, personalize their offerings, and boost engagement and sales. These same predictive tools are also used to better understand employee behavior, identify risk, and determine areas for improvement for businesses. This insight contributes to strategy refinements and improved performance. It also helps leaders and stakeholders make more informed decisions. To begin, organizations must first gather descriptive analytics.


Descriptive analytics reviews historical information to answer the question, "What has happened?"

Information is collected in a number of ways. Considered the following to build a baseline:

  • Surveys given to clusters of employees with similar roles or in the same department
  • Budget reviews over a period of years
  • Frequency and rate of employee turnover
  • Operational costs
  • Equipment failures
  • Customer ratings and feedback

After collecting this data, leaders can then create data points to adjust company strategy and set goals.

What Can Be Learned from Predictive Analysis?

The data collected from a descriptive analysis provides a foundation for further study. By plugging this information into machine learning software, your organization receives a comprehensive picture of your business' health. The software reviews your input and analyzes trends, showing you areas for improvement. With this crucial information, you can take action and anticipate future events. Using the results, business can make more informed decisions, forecast future needs, and identify potential opportunities. Machine learning software is available from third-party vendors or provided by a consultant or in-house data science team.

Employee Engagement

Employers may want to examine different data points to see what is causing turnover or low employee morale. Based on the trends, organizations may consider adjusting or changing policies to meet the needs of employees. For example, if a common frustration for outgoing employees is time spent at work, employers may institute remote work programs.

Ensuring Safe Workplaces

Safety is a key concern for all organizations. Predictive analysis can show what times of day or days of the week incidents were reported. This helps managers determine if more coverage is needed during those times. better understand how the work environment is affecting decision-making.

Addressing Resources

Businesses wanting to preserve resources can investigate which areas do not require lighting or temperature control during certain times during the workday. You may discover space is not used effectively and the physical layout needs to be changed for better productivity.

Related Reading: Branch Out and Brainstorm

The Benefits of Predictive Analysis

Businesses are always looking for a competitive edge. Through predictive analysis, organizations make faster, more informed decisions. The amount and frequency of data produced and maintained through technology is often overwhelming to sort and understand. When data is processed through machine learning, organizations get visibility into the small details of their operations. Additionally, the analysis provides transparency in the supply chain. This insight allows businesses to take action to change or improve processes through targeted, specific strategies.

Supporting New Initiatives

Employees want to know the "why" of adopting unfamiliar technology or processes. Data provides scientific support to demonstrate the benefit of new initiative, helping businesses overcome resistance. A comparison of results before and after a new initiative begins shows the purpose behind decisions and validates the changes taking place.

Anticipating Future Events

Workplace leaders can also anticipate system failures or facility issues that could impede progress. They can then advocate and budget for upgrades. Most importantly, they can direct resources toward improvement with more precision.

Related Reading: Pilot Purgatory

The Drawbacks of Predictive Analysis

Like any new or unfamiliar digital tool, predictive analysis requires a learning curve. Some third party vendors' price points are too high for small- to mid-sized businesses. Selecting a service to meet the need is a key decision. Often, a number of services these companies offer are either underutilized or irrelevant. Consequently, users may become frustrated and revert to legacy processes. This results in a diminished return on investment.

Data Depth

Historical data collection may be limited in size and scope, especially in regard to human resources information, which may only be kept for a handful of years. This missing data may lead to key variables being overlooked completely.

Managing Information

Organizations may want to take action on the conclusions made by the predictive analysis but don't know how to manage all the information. Without proper training and support, these efforts could be misdirected. To better understand the data, businesses hire data scientists, but the field is relatively new. Bringing a person with these credentials on the team could be cost prohibitive.

It's important to make informed decisions to get the best results out of your human-technology integration. Critical Ops provides business-use case testing to help you determine if your initiatives are effective and providing a high return on investment. We have partnered with several companies leading the way in linking businesses to emerging technology. If you'd like to try predictive analysis or a new tool or software and aren't sure if you're making the right choice, we can help! Contact us today for more information!


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