1. Let analytics tools prepare your data

Analysis always begins with gathering data and getting it ready for study and interpretation. Many organisations rely on analysts to combine and package data manually before they start Analysing 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 modelling.

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.

Analysing 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 Analysing historic data on behaviours 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 organisation 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.

4. Share business intelligence

Once you and your collaborators have drawn insights from data visuals, reporting software helps you share your business intelligence with people inside and outside of the organisation.

Reporting software that’s part of your data analytics toolkit lets you efficiently publish your findings, embed visuals, and assign permissions for those who need to see your report. It also simplifies report distribution, sustains version control, and avoids offline copies.

Create and distribute custom reports

Make reports your own with format and layout themes customised for your business. Reporting software also lets you publish in the way that works best for your communication, such as paginated static or interactive reports made in a table format. You can also optimise reports, making it easier for people to open and read on mobile devices.

Designate how often you want a particular type of report to go out, such as every week or once a month. Or make ad hoc self-service reports available that answer specific questions whenever people need those answers. A large-scale benefit of reporting software is that it can distribute reports created by analysts across your company to a large list of recipients.

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 analyse 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.