What is reporting in analytics?

Reporting is the process of collecting, organizing, and presenting operational data in user-friendly and digestible reports. The information gathered is related to specific daily business operations such as sales, payroll, inventory, and so on.

However, because these reports are designed to make data accessible in the quickest and most efficient way possible, the insights they provide are often limited. Consider them as a way to understand what is happening in your day-to-day operations, rather than why it is happening.

Companies generate a variety of reports, including financial reports, accounting reports, operational reports, market reports, and others. This allows managers and business-owners to study the performance of each function quickly.

Creators of these reports extract the data required to build them from operational systems, also called transaction systems. Operational systems are software packages that enable organisations efficiently execute business processes. Popular types include ERPs (Enterprise Resource Planning) and CRMs (Customer Relationship Management). The data generated by these operational systems become the source data for your reports. Oftentimes, these operational systems come with structured reporting features that give rudimentary summaries of activities in just that system.

Users may need to build more robust reports that combine historical data from multiple sources. For this, users typically resort to manually combining these data sources using a spreadsheet like Excel or other analytic tools like Tableau and Power BI to generate their reports.

A great practice to improve reporting outcomes is to use a standardized methodology for visualizing data, such as the IBCS (International Business Communications Standards).

Here is a typical report generated using these standards:

Source: Analytics Using Zebra BI Power BI Custom Visualisation

Every department in a company will use the data differently. For efficiency purposes, there should be a self-service approach to usage, ensuring that each department within the company can generate its own reports without the additional help of a dedicated IT team. Many of these operational systems allow users to export data to Excel or CSV files that can then be used to supplement the report with additional data if the tool cannot provide it.

What is Data Analytics?

While reporting is used to format and present historical data in an easy-to-understand format, analytics is used to compare and interpret data to help in the decision-making process. It gathers data from various operational systems to determine why the data is the way it is.

Analytics is about asking questions, examining, comparing, and interpreting the data. Perhaps most crucially: analytics should make it easy for you to recognize patterns. When combined with huge volumes of data from multiple sources, analytics can predict future trends and help generate more accurate forecasts. 

The data analytics process is all about studying historical and current data using statistical modeling, algorithms, and data visualization to gain valuable insights, detect trends, and help make informed decisions about your future direction.

Have you ever suspected that a line item in your dataset may be a key driver for changes in another variable? Data analytics helps you determine how items are related, also known as correlation. Discovering these relationships is the holy grail of data analytics. Choosing the right visuals goes a long way in showcasing your analytics, enabling users to see and understand why changes have occurred and what can be done to improve.

Here is an example of this in action. We want to analyze the relationship between wealth and health as nations develop across the world.The animation below has plotted Life Expectancy on the Y axis as a proxy for health and Per Capita Income on the X axis as a proxy for wealth. We visualize this for the entire world from 1900 to 2008 (108 Years). For a global busines, NGO or government, the insights gained from this visualization is an invaluable guide to making better data-driven decisions. This is the basis of what data analytics is all about.

Source: Gapminder Bubble Tool

Historically, data scientists derived insights by manually analyzing spreadsheets and documents. Nowadays, they can use analytics software that leverage machine learning (ML) and natural language processing (NLP) capabilities to quickly identify relationships in your data and deliver more insights in a fraction of the time and with minimal cost.

Manufacturing companies are a good example of how data analytics can be useful. By recording the runtime, downtime, and work queue of their machines and then analyzing that data, they can better plan workloads and even predict downtime before it happens so that their machines are able to operate at peak capacity.

Analyzing the Difference Between Reporting and Analytics?

Conclusion

Knowing the distinction between the two is critical for taking full advantage of their potential. Some key differences include:

– Purpose: Reporting entails gathering data to gain a better understanding of the performance of a company’s various functions. Analytics is the ability to interpret data at a deeper level to make better decisions. For example, a company can use reporting to evaluate progress on various marketing campaigns, then use analytics to better prioritize them.

– Process: The key distinction here is that reporting is about consolidating, organizing, and formatting data. It is used to simply extract data and present it in its original form. Analytics, on the other hand, is concerned with examining, questioning, comparing, and identifying trends from the data collected.

– Output: Reporting generates facts about the performance of different functions within a company. Examples of these functions can include sales, marketing, production, and so on. Outputs usually follow a structured reporting template and timeline (e.g. daily, weekly, monthly, quarterly or annual reports). Analytics takes it a step further and provides insights, detects patterns, and generates recommendations.

– Roles: Reporting is simple and is usually automated. Managers and department heads use the reports to track the performance of their companies. Business leaders and decision-makers use analytics to develop strategies, create policies, and optimize various functions.

– Technology: Reports can be predefined extracts from operating system or they can be designed in spreadsheets like Excel and are usually presented using PowerPoint. The report creation process can be automated with technology, e.g. the use of ETL (Extract Transform and Load) tools to automate the data extraction and cleanup process from different sources, then the use of business intelligence software like Excel and Power BI to visualize the reports in charts and tables.

Analytics can also leverage the smae technologies use to create reports (Excel, Power BI, Tableau, Qlik etc), but they also go a step further by also making use of advanced technologies features and statistics to detect and visualize patterns in the data. Analytics also leverage the power of machine learning and natural language processing to speed up the process of getting to actionable insights and data-driven decisions.

Conclusion

While both reports and analytics deal with data, they do so in different ways. The simplest way to understand it is that reporting shows what is happening while analytics explains why it is happening.

Reports are critical in the management of a business, establishing a reporting rhythm is important, automating the generation of reports is also very critical. The time saved by automating the generation of your routine reports will enable you spend more time performing analytics, allowing you to examine, explore, and question your data. A successful business must use both to monitor the performance of various functions while making data-driven decisions.

Developing an efficient reporting and analytics system can be a difficult undertaking. At dbrownconsulting, we have years of experience building Analytic systems that automate your reports, standardizing how you communicate your numbers succinctly and coaching to better question your data through effective use of analytics. Have questions about how we can help your team save precious time? Contact us today.z

This article is the first in a two-part series about the differences between Reports and Analytics. Learn more about the technical differences of each in Reports vs Analytics: Technology Deep Dive.

In today’s competitive business landscape, organizations are increasingly turning to analytics to give them the answers they need to make decisions with greater confidence. However, while the number of businesses taking advantage of analytics may be steadily increasing, the average person’s understanding of analytics remains vague.

So, what are analytics and how are they different from reports?

Simply summed up: “Reports provide data; analytics provide insight,” which is true, but not really helpful if you don’t know how, or why, this happens to be the case.

I have discovered that there are layers to understanding–the more technical you get, the more you can understand the difference and potential of analytics–so, I’ve split this topic into a 101 and a 201. In this article (101), we focus on the differentiators between reports and analytics, and the unique outputs of each. In the next post (201), we’ll look at the technical differences that make it all possible.

Transactional and Operational versus Insightful

The first significant difference between reports and analytics is the source of the data.

Your basic reports are run against the system in which the data was originally created, such as your HRMS, Learning Management System (LMS), or Accounts Payable system. These systems are transactional–or operational–meaning they are designed to help you efficiently perform certain tasks and keep records of those tasks. While these systems provide reporting options, it’s not their primary purpose.

“Reports provide data; analytics provide insight”

Take for example the hiring of a new employee. HR needs to create a record for that employee that captures their basic information, job title, and manager. This “hire” transaction–the creation of a core employee record–is performed in the HRMS. Once created, this record can then be accessed in other areas of the HRMS to perform new actions, such as benefits registration, IT provisioning, and payroll processing.

However, there’s no single record that includes every event or action for an employee–the system simply couldn’t operate if it had to access a giant file like this each and every time a user accessed an area of the system. Instead, the records of each transaction are split across the HRMS and only the ones necessary are accessed–a pay record here, benefits over there, and performance reviews somewhere else–each with a connection back to the core employee record, but not to each other.

Why?

Because the technical structure underpinning transactional systems are designed to facilitate system performance so you can complete all of your actions quickly: searching up a record, making a change, and saving that change. However, this same structure also makes it difficult (if not impossible) to create reports that are sourcing data from different locations within the system.

Data is stored separately to specifically optimize performance. Bringing the data of every event or action from different tables together in a report can slow down the system, sometimes to the point of stalling it entirely (in the past, I’ve had to open many a support ticket to have a server restarted after an overly-ambitious report caused it to grind to a halt).

There’s no single record that includes every event or action for an employee–the system simply couldn’t operate if it had to access a giant file like this every time

Analytic solutions, meanwhile, are designed specifically to connect data from different records (more on this in the Siloed versus Unified section below), transform and normalize it, and facilitate the exploration of that data. The restrictions and connections necessary in a transactional solution aren’t needed in an analytic solution; the data can be structured and organized to optimize analytics. Where your HRMS can’t store a single thread of every event involving an employee, a well-designed analytic solution can.

Data can be connected and joined in a fashion that optimizes retrieval, especially when coupled with a powerful query engine. For example, Visier’s query engine can execute complex queries literally millions of times faster than a traditional database.

Static Columns versus Dynamic Definitions

Reports in your transactional system are defined before you run them, and these definitions include which data columns will be included in the report. You may have controls on your report that allow you to set parameters for the report results, such as date range or by organization, but you’re still limited to receiving just those columns in the report.

If you want different columns, you’ll need to run a different report or, if you have access to the report writing tool, you’ll need to write your own report and then run it.

On the other hand, analytics display results for the selected parameters in a visualization intended to illustrate patterns and trends, bringing insights to the forefront without you or your team having to do any of the heavy lifting.

In addition, analytics encourage exploration, as well as the user’s natural inclination to see the results and ask themselves “I wonder what else there is?” The viewer can change or add filters to get new results in real time. In the image below, I added an additional metric of Employment Start Type and the results refreshed immediately.

Again, because analytics is dynamic, the user has the ability to drill even further into the details (as seen in the image below).

Siloed versus Unified

Very few businesses leverage a single system for absolutely every business transaction. More often than not, it’s an enterprise solution that’s used to perform the core functions (such as an HRMS for employee data, benefits, payroll), with niche, best-of-breed solutions, such as an LMS, employee engagement tool, or an Applicant Tracking System (ATS), used to manage other functions relating to the employee.  This means that there are employee-related records in multiple solutions.

When data relating to a single object (such as an employee) resides in different solutions like this, the only way to look at that data side-by-side is to pull separate reports and then, manually manipulate the files to create a single report. This becomes even more complex when organizations have multiples of the same solution (multiple payrolls, multiple HRMSs, and more). It becomes nearly impossible to get something as simple as an accurate headcount number!

An analytic solution brings all the data together. When it’s properly designed, it creates the connections across all of your data points, giving you a seamless and single point of access across your entire organization.

For example, it can be impossible to get measures on your quality of hire because your ATS can’t see what happens to the candidates once they’re hired and become an employee. This is due to their employee data residing only in the HRMS and other employee solutions.

But when you unify recruiting data with information from the rest of the employee lifecycle, you are able to follow the thread from the point of hire and beyond, and spot important trends that could impact retention, productivity, engagement, and more.

As-is Data versus Transformed Data

When you run a report against your transaction system, the data that is returned is exactly as it exists in that system’s database. These reports present a snapshot in time, which is great for giving you a read on what has happened up until today, exactly as it was captured. For example, How many people started? How many performance reviews were completed? How many people got 5s?

Analytics takes all this transaction data and boosts the value of the information so you can look at what has happened up until today, but also what could happen in the future. This is done by:

• • Transforming existing values

• • Deriving new values
  • Predicting future values

Let’s look at each of these in detail:

Transform

When transactional data is sent to an analytic solution, there is an opportunity to transform the data values that exist in the source system into new values, either to introduce standard values or to correct for common data-entry mistakes.

For example, an organization that has two solutions for performance–one using a 4-point rating scale and one with a 5-point scale–wouldn’t be able to look at performance across the organization in a meaningful way because top performers from the 4-point solution would just look like above average performers alongside the employees who got a 4 rating from the 5-point solution.

In order for the data to be combined or compared, transformation rules would be written and applied to the performance scores so that they are aligned on the same scale. So, to apply a 5 point scale across the board, the transformation rule turns a “4” from the 4-point solution into a “5” and a “3” would be transformed into a “3.75”

Derive

Once the data has been added to the analytic solution, the query engine (the mechanism that retrieves the correct data for the user, based on what analysis the user has chosen to look at) can derive new values from the existing metrics.

For example, an organization may choose to define the “Top Talent” metric to mean those employees with top performance scores, critical jobs, and a tenure longer than 5 years. The solution stores the data for performance scores, and critical jobs. When the user chooses to look at an analysis that includes “Top Talent,” the query engine first calculates tenure (based on the stored Start Date), then determines which employees meet the “Top Talent” qualifiers. This new metric is calculated in real-time each time the query is run, offering a more nuanced, value-added view of the performance data.

Predict

Powerful analytic query engines can also predict future actions based on the patterns of the past; the difference between reviewing who has left and predicting who will leave. For example, Visier uses the random forest predictive methodology which you can read more about here.

Insights–not data–lead to results

So, at last, we come back to the adage we began with: “Reports provide data; analytics provide insight.” What this article has attempted to show is that reports are the outputs of systems that are designed to perform actions–not provide insight.

Insight is the deeper understanding you get of the actions and behaviour behind all of the data points you’ve gathered. It is the ability to make out the big picture from the millions of brush strokes that created the painting.

Taken individually, an employee record tells you about one employee. A report on hundreds of employees tells you about hundreds of employees–but an analysis of all this employee data can illustrate the connection between new hire training and turnover. The insight comes when you can see that employees who have participated in the new onboarding training have a lower rate of turnover; therefore, it may be worth expanding the pilot across the organization. With this insight, you can make the business case for the program to your leadership.    

Insight is the deeper understanding you get of the actions and behaviour behind all of the data points you’ve gathered. It is the ability to make out the big picture from the millions of brush strokes that created the painting.

An analytic solution leverages all of the data created by your transaction systems so you can explore it from many different dimensions and get real insight into your organization. Because analytics connect data in a way that facilitates explorations, displays patterns and trends through visuals that enable understanding, normalize data to facilitate comparisons, and derive new values that boost insight, analytics are a much more powerful business transformation tool than reports could ever be.