When I work with clients on data analytics projects, I usually start by asking a question: “Can you tell me what your most profitable product is?” It’s seems like an easy question to answer, and clients usually give me their best-selling product, but it’s not as simple as the product your organization sells the most.
When you’re identifying your most profitable product, you need to know how much that item costs you — those costs can be operating expenses, man-hours, storage, manufacturing or any other item that contributes to the financial footprint of that product. Chances are, when you start thinking about these factors, the answer suddenly become more complicated then at first glance.
This is why business intelligence is so important for every organization. If you’re analyzing the right data, you’ll be able to quickly understand which product is your most profitable. That knowledge will allow you to make smarter business decisions going forward.
If having to find all the right data and analyze it seems overwhelming, that’s understandable. A few years ago, if you wanted to find your most profitable product, you’d have to find and pull all the relevant data manually. Then you’d have to analyze the data yourself and put the report together. That’s a time-consuming, expensive, labor-intensive project, and because of the nature of data the resulting report would have a short shelf life. After all, as soon as the data changes, your previous report becomes obsolete.
Fortunately, tools for data analytics have improved in recent years, and there are a variety of them. Some tools allow anyone — even someone who is intimidated by numbers, to pull data and run simple reports. Some produce sophisticated reports and data modeling but require data scientists. All of them allow organizations to run reports monthly, weekly, quarterly or whenever they need it.
What tool is right for your specific business? That will depend a lot on how many data sources you’re analyzing, how big your company is and the technology you’re already using.
While there are many BI tools on the market, this blog post will spotlight five:
- Snowflake (AWS / Azure)
- Power BI
Before we delve into this list, however, a word about trying to manage your own data: Many business owners aren’t intimidated by data at all. In fact, they may have been collecting data for a long time, keeping it in a spreadsheet and analyzing it themselves. This is obviously a low-cost solution but if you’re getting your business insights with an Excel sheet, you’re missing something. For one thing, you’re not getting your data in real time, so the data in the spreadsheet is already old news. For another, as a human being, you can’t get some of the insights from your data that BI can draw from it.
Google’s BigQuery is a unique data tool. Unlike the other offerings in this post, BiqQuery isn’t software you buy and then use to conduct an unlimited number of queries. Instead it’s a web service that allows you to interactively analyze huge quantities of data, and you pay by the query. If you run a report, you only pay for the processing time it took to create that report.
BigQuery is a great tool if you don’t want to manage your own data warehouse, or if you’re analyzing a lot of data and need the full force of Google’s computing power to run massive reports quickly. However, it isn’t available everywhere, and many of the features are still in beta.
Snowflake is a fully relational ANSI SQL data warehouse that allows you to leverage the skills and tools your organization already uses. Updates, removals, analytical functions, transactions and complex joins give you the full capabilities you need to make the most of your data.
Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. It is a true data warehouse-as-a-service running in the cloud. With built-in performance, there is no infrastructure to manage or knobs to turn. Snowflake automatically handles infrastructure, optimization, availability, data protection and more so you can focus on your data rather than managing it.
Tableau is the biggest player in the data market, and it’s popular for good reason — it’s relatively easy to learn, offers excellent data visualization and has a variety of integrations. Tableau plays well with a number of other platforms (it integrates with BigQuery for example, but it also integrates with non-analytic platforms like Slack or SalesForce), and can handle most types of data.
The drawback of Tableau is that the software doesn’t cope well if you’re trying to analyze data from more than three or four sources. If you’re trying to analyze Big Data, or several data sources, Tableau might not work for you. If you have only a few important data streams from external applications, however, Tableau might be your best bet.
Looker is a flexible BI tool that combines data modeling with data analytics. Essentially, Looker lives inside your database. Its modeling language, LookML, allows data teams to define the relationships in their database so that any users can work with your company’s data without having to know SQL, the language used to build relational databases. It also allows you to embed data modeling and analytics in your existing apps.
Looker offers a solid BI suite of features, but it is also fairly expensive. If you’re a smaller company, or on a budget, it might not be the right data tool for you.
Power BI is Microsoft’s data analytics application. It comes included with many enterprise versions of Microsoft Office and allows users to visualize that data in dashboards. The dashboards are built using a drag and drop interface.
While Power BI can connect to a variety of external data sources, it really shines in a business that’s a Microsoft shop, because Power BI is built to handle data from Microsoft’s applications. If your organization uses Microsoft Office, you’ve probably already have Power BI, so it’s a good tool to try if you want to get started with data analytics.
Business Intelligence and your company
Business Intelligence can be intimidating — your organization’s applications and devices are constantly generating data, and if you’re not a data scientist, it’s hard to know what do to do with all that information. You might be afraid of choosing the wrong tool or making a mistake.
It’s worth remembering that there’s one big mistake you can make that will have a truly adverse effect on your company: being so intimidated by data that you don’t use it. After all, if you’re not leveraging your data, your competitors will.
How do you choose the right data tool? Ask yourself these three questions:
- How much data does my company have?
- Where is that data coming from?
- Who needs to see that data?
If you can’t answer these questions, Saggezza can help. Contact us today at firstname.lastname@example.org, and our experts will be happy to help you.
About the Author
Veera Budhi is an analytical and highly adaptable management professional with 22+ years of extensive experience enhancing business outcomes across enterprise. Skilled in aligning end-user needs with long-term resolutions to complex business challenges. Track record of success in strategizing the plan, architecting the solutions, leading development and implementation teams aimed at improving quality and efficiency at organizations. Advanced expertise in occupying leadership roles in all facets of Sales, Software development, project and product life-cycle management. Accomplished communicator skilled in building and strengthening relationships across functions to drive cohesive, strategic operations.
Saggezza is a proven technology and consulting partner that delivers personalized, high-value solutions to accelerate business growth.