Data has become one of the most powerful driving forces behind every modern business. Whether running a niche apparel boutique or an online fashion store, you need data to run and grow your business.
Effective analysis of data helps you derive actionable insights, which, in turn, facilitate smarter decision-making. It comes in handy in every aspect of your business, from launching a new product and targeting a new demographic to improving your profitability. It eliminates the need to rely on your gut feeling while making crucial business decisions.
However, the growing popularity of data analytics means your competitors are also likely to use it. So, outlining a data analysis strategy isn’t enough. If you want to gain a competitive advantage, you must ensure that your data analysis strategy is fast and efficient. It’s the only way to ensure that you harness the full potential of the data you’re collecting.
In this blog, we’ll outline a few valuable tips to speed up data analysis for your business. Let’s dive right in.
Audit Your Existing Processes
Before you make any significant changes to your strategy, you must take a closer look at your existing data analysis process. Are you storing your data in a data lake or warehouse? Or are you storing it in a delta lake to use ETL functions? What methods are you using to collect and structure your data?
Make sure you know the answers to these questions before modifying your data analysis strategy. It’s a good time to look at various sources that generate useful data and the journey your data goes through after reaching data lake or warehouse. It’ll help you identify gaps and inefficiencies in your existing processes.
Here’s the thing – you can assemble a team of the best data engineers and scientists and still struggle to rise above the competition. In today’s fast-evolving world, you must use the right tools to derive maximum value from your data. It helps automate various routine tasks and eliminates room for human error.
Start by identifying the best platform to store your data. When you’re dealing with multiple data sources, Excel spreadsheets won’t make the cut. Instead, you need a specialized data storage solution like a data lake or delta lake.
Data lakes help store raw data and enable easy access from different compute engines. On the other hand, a delta lake is suitable when you want to prevent data loss due to ETL functions. Alternatively, you could use a data warehouse to collect all your data in a centralized dashboard and keep it ready for processing.
Once you’ve selected the most suitable storage solution, it’s time to look for the best analytics tools. While you’ll find tools with plenty of sophisticated features, make sure you choose one that addresses your needs and is compatible with the storage solutions you’re using. Also, find out whether it offers data visualization and reporting capabilities.
Define Measurable Goals
As with any business strategy, you can speed up your data analytics strategy by outlining clear and measurable goals. It’ll help you understand whether your strategy is headed in the right direction.
Do you want to use it to increase sales and profits? Or do you want to test the viability of a new product? Or would you like to boost audience engagement on various online channels?
Answering these questions will help you set the right goals for your strategy. You can use the SMART approach to ensure that your goals are easily measurable. Also, make sure your team members have a clear understanding of these goals.
Clean Your Data
Let’s face it – you don’t have to collect and process data from every system that’s a part of your organization. If you indiscriminately store data, your data lake/warehouse will soon be crowded with irrelevant and unusable information.
That emphasizes the need to regularly cleanse the data you’re collecting from different sources. The key is to understand whether a particular data set will help you achieve your goals. If the answer is no, you’re better off eliminating it.
Furthermore, it makes sense to perform basic hygiene checks on the data you decide to retain. Make sure it’s accurate and consistent across different systems. Also, you can structure it to make it accessible for different tools.
Speeding up your data analysis process isn’t rocket science. All you need is a clear understanding of why you’re doing what you’re doing. Also, it’s crucial to build a dedicated data analysis team and provide the necessary tools. Lastly, make sure you monitor and clean your data at regular intervals.
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