What is Analytics?
It’s common knowledge among enterprise companies that robust reporting and analytics are necessities. They need solutions to manage and monetize the extensive data generated today. Indeed, as of 2022, internet users generated 2.5 quintillion bytes of data each day, a huge amount of which is on organizations’ websites and through their customer-facing platforms.
But do we even know what reporting and analytics are? Organizations often use them interchangeably. Are they really the same? Does each require the same tools?
Let’s get into the nuances of an ideal tech stack to help business and data leaders drive value from their reporting and analytics solutions.
What is Reporting?
Reporting presents information in a precise and standardized way. It should be clearly understandable and relevant to stakeholders. Primarily, reporting operationalizes data through established metrics and Key Performance Indicators (KPIs) to let users decide the best course of action on a day-to-day basis. Which means that reports give a zoomed-in view, so users can take direct actions informed by data.
For example, business teams use financial reports like accounts receivable and customer aging to see which clients have currently open invoices and how long they’ve been open for. Based on this data, staff can take immediate action if they see customer collection rates dropping for specific customers by sending 90-day notices or passing account information along to customer service teams.
Here you can see how using a financial report has a direct impact on daily operations.
What is Analytics?
Broadly, analytics is the practice of examining historical trends to predict the future for long-term decision-making. The field has become even more diverse with the advent of data science and machine learning (ML), which attempts to give more meaning and context to data to help determine next steps.
Where reporting is granular, line-level, and operational, analytics is broad, aggregate, and strategic. This is not to say that analytics isn’t operational, or reporting isn’t strategic, just that each is better tuned for these specific traits.
Key Differences between Reporting and Analytics
The differences between reporting and analytics come down to four categories – Purpose, Method, People, and Value.
Purpose
The purpose of reporting is to tell, “What is happening” by collecting information from various sources and presenting it in navigable levels of detail. It takes a more hands-on approach, as it allows users to get into the line-level details of specific accounting or operations data.
For example, a sales report would show sales figures for each customer, line-by-line, in specific periods of time. Users can easily carry out custom calculations against each customer to derive ratios, averages, probabilities etc. to decide what to do next. As the name suggests, it gives an account of the current state of things in as fine of detail as needed. Done correctly, a user should be able to navigate from a high-level accounts receivable report all the way down to a specific customer invoice without ever leaving the context of the financial data.
Analytics will be more expansive in its effort to extract valuable patterns from aggregate information. For example, sales figures may show fluctuations over years that correlate customer retention with price rates. The perceived patterns will help decision makers determine the reason for this and predict sales growth for the following year.
Because of the generally larger amount of data used in analytics–as opposed to reporting–it is sometimes more “readable” when presented by charts and graphs, rather than in tabular spreadsheet forms. This is where data visualization comes into play.
Method
Reporting usually involves collecting, consolidating, organizing, and manipulating data. While there often exists an inclination toward visualizations such as dashboards, those workers who consistently get their hands on the data tend to prefer formats that let them manipulate numbers on varying levels of detail–tools like the spreadsheet are ideal here.
For analysis, specialists may create sophisticated mathematical models or run complex statistical calculations to get meaningful results from data. These models will sit behind charts, graphs, etc., highlighting trends and correlations within typically very large data sets. Because analytics is concerned with trends, line-level presentations are not ideal.
People
Both reporting and analytics are meant to help managers and executives in decision-making. But, the people responsible for reporting may be different from the ones who do the analysis. This is more often the case as organizations grow in size and roles and teams become more specialized. A business analyst in a large enterprise, for example, will be less concerned with specific reports and more concerned with large-scale trend analysis using aggregate data. A financial accountant, however, would want access to highly detailed granular data that gives them a clear accounting of who owes what and how to take specific actions in relation to specific accounts.
When we think of roles or job titles with their hands on reports, we think of accountants, controllers, people in purchasing, customer relationship staff, etc. Those concerned with analytics will often be the C-Suite, directors, board members, etc.
Value
The value of reporting is that it shows exactly the current state of business across a number of changeable and detailed parameters. For example, how many widgets are currently on the shelf in the Dallas warehouse against how many orders for widgets are due to go out from that warehouse.
Analytics helps in future planning and direction. It forms the basis of many business strategies and provides insight for optimizing operational efficiency. An example would be looking at five years of widget sales from the Dallas warehouse and making decisions about increasing or decreasing warehouse capacity based on a predicted number of sales five years into the future.
Components of a Reporting and Analytics Tech Stack
OK, so how can enterprises build a robust tech stack for their reporting and analytics needs, given how different these roles actually are? Following are five essential components we see as crucial to a thriving tech stack. This list is neither exhaustive, nor specific, so feel free to chat us up if you have questions about your unique needs. We love a good data chat.
- Data Source(s): Organizations must identify their data sources based on the KPIs most important to them. Social media, customer relationship management (CRM) applications, enterprise resource planning (ERP) apps, and cloud and on-prem databases all must be considered. Further, is the data structured or unstructured, and are there plans to migrate to the cloud? Answering these questions will help companies build the right system.
- Data Storage: The right framework for storing data will depend on a constellation of factors–a topic far exceeding the scope of this post. However, typical data storage frameworks boil down to databases, data warehouses, or data lakes, or some hybrid approach.
- Data Integration: A secure system should be in place to bring the data from multiple sources into data storage platforms. The data engineering team (or, often, a consultant or services firm) is mainly responsible for building durable Extract, Transform, Load (ETL) pipelines to transfer data into repositories for quick and easy usage.
- Data Governance: Any successful data stack requires a sound governance system. This helps determine data accessibility and ensures data integrity along the data pipeline as it travels through the source to storage platforms.
- Data Usage: The end points. Reporting and analytics, as we know, are the two primary uses of data. Teams can create automated dashboards and reports, and use analytical tools to interpret data more meaningfully.
Implementation Strategy
So now we have a working theory about the uses and components of a reporting and analytics tech stack. But how does that tell us anything about implementation? See below:
Goals and Objectives
The first question is what goals or objectives we have. For starters, everything from data sources to governance strategies should serve the final purpose of reporting and analytics.
But then what metrics are we reporting on and analyzing for? Are we going to leverage every data point we have? Definitely not. Business and data experts must identify where reporting and analytics are necessary and to what extent the data should be collected, stored, and funneled to end-points.
This requires relevant guidelines and standards to help teams create specific reports for specific purposes and perform customized analyses tailored to a particular need.
Choosing the Right Data Storage Platform
Enterprises should know the amount and type of data they have. They can then decide what type of data storage platform would be suitable.
Should it be open-source? Should it be cloud-based, such as Amazon Web Services (AWS)? Or custom-built internally? Can it integrate with the current Software-as-a-Service (Saas) based applications?
Overall, the tool should be customizable, scalable, and cost-effective.
Data Integration Tools
Enterprises again have the option to build entire ETL pipelines from scratch or purchase a third-party tool. Using third-party solutions can help get you running quickly but can struggle when the complexity ramps up.
Data Governance Frameworks
Several data governance frameworks guide how to manage data effectively. Enterprises should select the one that best fits their culture, skillset, industry, etc.
The proper framework will allow different teams to access the correct data at the right time and ensure that the enterprise complies with data regulations. This is a complex affair, and a highly custom one, but don’t think it’s solvable right away. Often this process is highly iterative.
Tools for Reporting Versus Tools for Analytics: The Good Stuff
It’s common to think of Tableau and Power BI when talking about Analytics. However, these are visualization tools primarily. They provide visually appealing dashboards to view data in particular formats through nice graphs, pie charts etc.
Such tools are good for presenting information to executives who don’t want technical details. However, reporting goes on behind the scenes to fuel such visualization tools.
A dashboard on PowerBI may show revenue projections for the next month. However, calculating these projections would require financial analysts to work on Excel reports where they can see revenue streams for each day or month along with other variables like prices, the number of items bought, cost of producing each item, etc.
So a reporting solution should allow the users on the front-lines to have more granular control over data–where they can easily slice and dice the data as needed.
For analysis, open-source tools like Jupyter notebooks, MySQL, RStudio, or even Excel Spreadsheets are useful. Data scientists use Python or R with MySQL to fetch data from a data warehouse or data lake and perform analyses.
These programs require a good bit of technical knowledge though, and expecting business users to wield them is, well, a lot. If your organization has an IT team, these types of tools would sit there and let IT provide the platforms onto which analytics tools are then positioned. Not everyone has this capability. Many times, a third party contractor is required to bring in and service a system to these ends. Choose wisely, with the knowledge that reporting and analytics are vastly different and not many out-of-box tools will service both (or either) in truly scalable ways given customized needs.
In short, no single tool can perform both analytics and reporting. Organizations must look for different specialized tools instead of finding one holistic platform. This almost certainly requires outside help.
The Crux of It All
Due to its sheer volume, driving value from raw data today is highly challenging. Enterprises must build scalable solutions to handle such an extensive amount of data.
But scalability calls for the automation of data workflows. Reporting and analytics solutions should have real-time data ingestion layers that constantly update the relevant reports.
Interject does this by integrating spreadsheets directly with data sources. Effectively, the spreadsheet becomes a living data interface. Combined with governance best practices, the solution helps develop valuable automated reports.
We provide a toolset for creating and publishing reports and apps that can be leveraged by IT, by consultants and implementation experts, by software companies who need reporting offerings, and by our own services team. We’ve seen tremendous success with this model in fast-growing companies. But our principles should apply regardless of the apps and strategies your company leverages.
The ultimate goal is wider accessibility of data, centralized control, and more simplicity for end-users. We want to break down the barriers between needing key reports and having key reports, whether your a consultancy, an ERP seller, or a large company with in-house IT.
We’re here for your reporting and data needs, and we can help get solutions to users faster and more effectively when your ready, so don’t hesitate to reach out.
Contact the Interject Solutions Team at info@gointerject.com