Make Room for Big Data–Customer Stories on What’s Important in Data Analytics Software

Make Room for Big Data–Customer Stories on What’s Important in Data Analytics Software


Who doesn’t love a good story? Imagine the power of visual stories–photography, videos, charts, and graphs. The surface of data analytics begins with similar visual storytelling. Data stories capture business intelligence, turn into knowledge and organizational wisdom, and lead folks down the path of predictive analytics.

Now capture this imagery with the impact of customer perspective and experience using data analytics in the workplace to reduce the amount of injuries, illnesses, or other EHS data.

With data visualization from firsthand experience, you can take the first step in analyzing the root of your story and uncover trends to prevent future incidents from occurring. Which chapters have Gensuite subscribers discovered are most critical to developing a successful data story in their workplaces?

Chapter 1: Good Data & Clear Reports Data visualization is a lot like graphic design. When you first see a website you often decide to continue viewing it within 30 seconds or less–the same goes for successful reports. If you can understand a report in 30 seconds or less, then you have achieved one element of a successful report. High-level managers that view these reports often do not have extra time to further analyze convoluted charts and graphs. Keep them simple, easy to understand, universal team-wide and across the globe. This means finding an analytical software system that can generate reports built on interactivity using dashboard actions with cross-app reporting functionality. All of this builds trust in your employees, in the data, and data analytics software system you use.

Chapter 2: Statistics & Data Data analytics is a lot like the literary device, the iceberg principle. Charts and graphics represent 20% of the above-the-surface story and the other 80% is below the surface. This underlying 80% represents data management, data hygiene, and all the stats, data, and reporting. Below the surface, the structure is set up in a pyramid: Data, info, knowledge, wisdom.

First, take your raw data and then use it to make intelligent business decisions. Apply it in meaningful ways to gather more information. Use data to uncover certain information about particular geographies and how it affects those areas. From there you can begin to generate knowledge about what is happening in a particular area. In due time, you will gain wisdom about making the right decisions on how to stop certain trends from happening, or what occurrences matter the most to you or in that aforementioned area. Following a structured pyramid ensures data integrity and less invalidated data. When you and your site managers better understand raw data and the information your employees share, you can build programs around the knowledge learned. This creates a safer work environment and enables more efficient production of goods.

Chapter 3: Predictive Analytics By combining the 20% and 80% elements of data analytics above, you can start to make assumptions from the visuals and processing of raw data and begin to create possible outcomes to further evaluate within data analytics. That’s discovering what matters most in your organization to make predictions and prevent future incidents.

  • One company piloted a new Gensuite data analytics application, called Tag-it-bot, which ran bots for specific search terms in custom groups and data mined for certain injuries. The bot then generated reports on a regular basis within these specific terms. The regularly reported data allowed this company to begin to see trends within specific areas that mattered most to their organization.
  • Another company with 400,000+ employees, 90,000 users within Gensuite, and millions of data records per year had to find a way to simplify their reporting structure. This company wanted to reduce 30 metrics down to six. By using a data warehouse, they were able to filter through millions of data records and discover which six metrics were most important companywide. Those six metrics improved processes overall and lowered incident and illness rates. The metrics were both leading and lagging indicators and looked beyond EHS, as the business has continued to improve their employee turnover rate.

By understanding the components of data analytics most valuable in the workplace, you will be able to better communicate and leverage the results. That means fully understanding the iceberg principle and knowing the visuals that represent 20% of data analytics, and the other 80% below the surface–and then fully engaging in a complete look at your data and proper data analytics to create a successful, safer, workplace. Leverage the metrics and tools that work for you! And remember, metrics speak and tell a story in every language.

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