1. Let analytics tools prepare your data
Analysis always begins with gathering data and getting it ready for study and interpretation. Many organizations rely on analysts to combine and package data manually before they start analyzing it. But even when great care is taken to include all relevant data, a manual process risks missing vital pieces of information or inadvertently including incorrect data.
Get better results with connected data
Data analytics software enables you to aggregate self-service and enterprise data in one view, even when it comes from multiple places. Make sure the reporting tools you choose have the capabilities to bring all your data sources together, including Excel queries and data modeling.
Once you have the data you need all in one place, comprehensive data analytics tools can efficiently cleanse it to make sure it’s complete, accurate, up to date, properly formatted, and free of duplicated or irrelevant information. That saves a lot of time for your analysts.
Draw meaning from big data reports
The sheer amount and variety of data that companies accumulate now, and the speed with which it generates every day, make fast and clear analysis a necessity. Data analytics tools that are designed to scale can handle big data in a way that traditional databases can’t.
Analyzing large-volume data sets is also challenging if you need to include a constant influx of information from multiple sources. Data analytics tools help you interpret massive amounts of data on broad topics such as current market trends or the preferences of customer segments.
Get deeper insights with AI
As you research data analytics software, look for tools that incorporate artificial intelligence (AI) and machine learning. These help your project collaborators and company understand past performance, make predictions about the future based on that knowledge, and plan proactively.
Better understand customers
Individual predictive analytics help you know your customers better and reach them with greater efficiency and effectiveness. These augmented analytics use AI and machine learning to help businesses anticipate future market demand by analyzing historic data on behaviors and preferences of customers in different groups.
Some data analytics software provides predictive AI capabilities that don’t require any code training or experience, making it possible for more people across your organization to use them. This helps non-technical business professionals prepare data and extract valuable customer insights. It also benefits technical professionals by enabling them to do these things faster.
Benefit from advanced AI
Advanced statistical analytics gives your data scientists and machine learning engineers ways to make advanced data models, build better visuals, learn more from data, and enhance planning. This kind of AI capability is supported by the R open-source programming language.
5. Protect your data
You may have concerns about security when you think about connecting large quantities of data from multiple sources and later publishing business intelligence. Look for data analytics tools that have safeguards to protect customer data, and encryption keys that keep sensitive data private while in transit.
Data analytics tools with built-in protection benefit you by blocking risky activities that pose threats to data. When you share reports, those tools can provide workspace-level security by publishing to a separate workspace. They also enable row-level security by restricting data access at the row level, and letting you set broader access controls where you need them.
Choose tools that let you analyze data with your governance features working in the background. And make sure your tools help you comply with the standards, regulatory controls, and certifications of your industry, like IL6, FedRamp, HIPAA, and FINRA.