In the business landscape of 2023, data is the only currency that matters. The success of any business into the next year and beyond will depend entirely on the volume, accuracy, and reportability of the data they collect, and how well the business can analyze, extract insight from, and take action on that data.
But the foundational step in getting the data to drive your business forward is first ensuring it can be collected and identified in a way that makes it simple to find and report on with the insights that matter.
Whether the reporting is being done by an end user, a data science team, or an AI algorithm, the future of your business depends on your ability to use data to drive better quality for your customers at a lower cost. So, when it comes to collecting, storing, and analyzing data, what is the right choice for your enterprise? The decision will come down to a database vs. a data warehouse, but let’s start by explaining what each is and why they are used.
What is a Database?
A database is, by definition, “any collection of data organized for storage, accessibility, and retrieval.” Databases usually consist of information arranged in rows, columns, and tables, organized mainly for easy input and collection of different events. Common databases that most of us use in our everyday lives are relational databases, which includes ERP and business process management systems, SQL databases, CRM systems, and even static Excel spreadsheets.
A database holds multiple tables, each consisting of columns and rows. Each column is assigned to an attribute, and each row holds a single record. For example, imagine that you have a database collecting transactions by customers. The columns will specify the attributes of those records and activities (customer name, customer number, salesperson assigned, amount of the transaction, date, etc.), while rows will contain the individual events and trades themselves. Furthermore, that same database could have an entirely new section devoted to tracking similar transactional information by item, but contain further details about the items location, shipping, supply vendor, and more. This is how databases function essentially, with multiple different tables cobbled together with keys that can help queries understand the relationships between them. To report on that data, you need to understand not only where the data resides, but also the relationship between these tables and their dependencies.
Databases can be stored either on a local server or in the cloud and can be access for reporting in many different ways, through limited native tools included with the system collecting the data itself, to Excel exports or various direct connectivity options. Relational databases are incredibly useful for running a business; however, they are not optimized for getting information out. That makes the process of building reports from multiple tables or multiple databases time-consuming and tedious—if not impossible—for non-technical staff. Table-based reporting routinely causes performance issues as well, particularly with large data sets.
Now that you’re familiar with databases, let’s take a look at how they compare to their bigger, brawnier cousin: data warehouses.
What is a Data Warehouse?
High level, a data warehouse is a collection of business data from multiple sources used optimized for reporting, analytics, and decision making. Unlike a database, a data warehouse’s architecture is built for getting the data out, and not just through technical expertise, but for common users like management, executives, finance professionals, and other staff. As the foundation for business intelligence and analytics, it extracts data from your existing data sources (databases), specifies a set of rules to transform that data, and then loads it into one central repository for you to quickly access and control. This automated process of extracting, transforming, and loading data into a data warehouse is commonly called ETL and it’s a huge advantage for analyzing your data.
A data warehouse stores transactional level details and serves the broader reporting and analytical needs of an organization, creating one source of truth for building semantic models or serving structured, simplified, and harmonized data to tools like Power BI, Excel, or even SSRS. While databases use Online Transactional Processing (OLTP) to store current transactions and enable fast access to specific transactions for ongoing business processes, data warehouses also enable cubes to store large quantities of historical data, automate and pre-calculate evaluations of that data, and enable fast, complex queries across that data.
A data warehouse is typically used by companies with a high level of data diversity or analytical requirements. Common data transformations such as standard costing, currency conversions, unit of measure conversions, and other business approved and validated calculations are all built into the data warehouse and its cubes, ensuring that reports are truly displaying the expected results. The dimensional model design of a data warehouse allows for the implementation of slowly changing dimensions, displaying the state of the various transactions and attributes exactly as they were at that point in time.
The only downside about a data warehouse is that, historically, it has a reputation for being complex, time-consuming, and expensive to build and maintain. The good news is that nowadays you can find business intelligence solutions with pre-built data warehouses to eliminate complexity, significantly reduce cost, and decrease risk.
Enhancing a Data Warehouse with Cubes
To manage all the integrated data inside a data warehouse, many companies build cubes (OLAP or tabular) for quick reporting and analysis. A cube is a multi-dimensional section of data built from tables in your data warehouse. They contain calculations and formulae that are often grouped around specific business functions; one cube for sales, one for purchasing, another for inventory, and so on, with each cube containing contextual, pertinent, and useful metrics for that particular area of the business.
Cubes are a great way for non-technical users to access data and report on because of the way they are structured: The heavy lifting is already done through pre-calculation. When you want to get answers from your data, your request goes directly to the appropriate cube. Reports that used to take five minutes to generate are now assembled in seconds, and end users no longer need to understand the complex web of references tying multiples tables together.
When organizations start to collect data in multiple databases, the size of the data sets grow exponentially. Running a standard query against large data sets from the live, relational database directly causes serious performance issues which not only sacrifice productivity, but can lead to users abandoning reports altogether. When this happens, important insights are discarded because users simply do not have the time for the data to be compiled. When utilizing cubes, whether looking at yesterday’s sales transactions or sales over the past five years, it takes the same amount of time to run your analysis, just a few seconds in most cases, thanks to the power of pre-calculating the values.
Database vs. Data Warehouse
As the complexity and volume of data used in the enterprise scales and organizations want to get more out of their analytics efforts, data warehouses are gaining more traction for reporting and analytics over databases. Let’s look at why:
Data Quality and Consistency
Data warehousing involves converting data from numerous sources, standardizing it, subjectizing it, organizing it, and ensuring it’s sorted and tagged by uniform constraints. This assures greater trust in the data being presented, reduces organizational blind spots, and provides greater opportunities for collaboration as individual business units like sales, marketing, and finance all rely on the same data repository reporting. Organizational alignment will be at an all-time high as siloed departments are finally able to use the same data to reach the same conclusions.
Superpowered Business Intelligence
One of the biggest benefits of data warehousing is the increased scope and reliability of the data stored. By improving access to your organization’s data, you’re improving the ability for leadership to execute a smarter strategy based on a more complete, and accurate, picture. By utilizing data warehousing, businesses can better correlate data from disparate systems to inform end-to-end business decisions that take every factor into account. Business intelligence powered by data warehouses provide greater insight into your supply chain, sales process, financial health, and more.
The use of data warehousing enables businesses to save more on their analytics and, consequently, generate a higher amount of revenue. As the cost of data warehousing decreases, this impact stands to increase exponentially, and by using data warehousing and BI software in conjunction to essentially democratize data and trim headcount in analytics and reporting functions, businesses can yield a return on investment sooner than ever before.
Data warehouses are built for speed, specifically to offer large organizations rapid access to data retrieval and analysis. Rather than dedicating valuable computational power to editing and managing individual data records, data warehouses are all about being able to access, collate, and analyze the data as quickly as possible, ensuring critical business decisions can be made in an instant and decision makers aren’t squandering precious hours waiting for queries to load.
If you are a Microsoft Dynamics customer, your relational database is doing the job it was designed to do: handle transactions. If you’re looking for a solution to help you analyze that transactional data, we highly recommend considering a data warehouse.
Knowing that not everyone has the budget or technical manpower to build a data warehouse and cubes, insightsoftware created a reporting and business intelligence solution that provides a pre-built data warehouse and cubes set that is ready to use out-of-the-box. Along with an extensive library of dashboard and report templates, Jet Analytics is designed to give you valuable insight into your data from day one.
In the coming years, the quality, consistency, and accessibility of data will be the difference maker for businesses of all sizes, so organizations will want to ensure they’re setting themselves up for success in the future by choosing the right infrastructure and storage.
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