Data analytics tools convert a database of raw numbers into valuable information that fuels business growth. A good analytics solution will automatically transform data into visual presentations that are easy to understand without specialized training.

Businesses use predictive analytics to predict when machines will break down, for e-commerce companies to track customer behavior online, and for healthcare organizations to evaluate patient health trends.

Automated Analytics

Automatic data analytics solutions involve the use of computer systems to handle analytical tasks rather than relying solely on human input. This approach eliminates human error and can significantly reduce the time required to complete a task, which can be especially important for processes that are prone to manual errors. It also helps enterprises improve their processes and systems by skipping steps that are prone to error and automating them instead.

The first step in implementing automated data analytics involves identifying candidate analytical processes to be automated. These candidates should meet the four criteria for successful automation: they must be repetitive, have business value, impact the bottom line, and offer the opportunity to iteratively refine the process over time. Once candidates have been identified, they can be used to test and build a prototype automated analytics system.

Once the system is up and running, it can be monitored for performance and functionality. This allows for a smooth transition to full automated data analytics and ensures that the new system is working as expected. Once the initial automated system has been tested, it can be extended to other areas of the analytics platform to further streamline and enhance the process.

In the world of big data, automation is a critical part of any analytics pipeline. It can help reduce the turnaround time from request to insight and free up data scientists’ time so that they can focus on surfacing new insights that can be used to inform decision-making.

The best data analytics solutions integrate automated analytics into the entire analytics journey. This means that data is gathered and transformed into an easily-accessible format for visualizations, analysis, reporting, and decision-making. This reduces the amount of work required for the end user and allows for more meaningful, creative tasks to be completed by humans.

Depending on the needs of the enterprise, this may mean automating the entire analytics workflow or just parts of it. It can also include the creation of dashboards that can provide a real-time snapshot of current state data. It can also include AI-assisted data preparation, natural language generation, and augmented analytics capabilities that can help people of all skill levels find the insights they need to make on-the-fly decisions.

Consolidated Data

Data analytics solutions based on consolidated data can save time for your analysts and improve the quality of data. By consolidating data, you eliminate redundancies and inaccuracies that can affect reporting or analysis results. This will help you to improve business performance and reduce operational costs. Data consolidation also helps to identify new business opportunities.

Data consolidation involves bringing data from various locations, databases, and files into one place for easier access. The process is important because it unifies data that is gathered from multiple sources, improves the accuracy of reports and insights, and speeds up the execution of business processes. The key to a successful data consolidation is to use it to break information silos and allow more stakeholders to access the data.

When you consolidate your data, it can be stored in a central location like a data warehouse for easy access by multiple users. This makes it easier to access and analyze the data, which will lead to faster decisions and better ROI. It also ensures that the data you are using is accurate and up-to-date.

The most common method of consolidating your data is through an ETL (Extract, Load, Transform) process. This is done by a data engineer who scripts a code that will send the data from various data sources to a single location.

Another option for data consolidation is to use software tools. These can be local or cloud-based, and can automate many of the data consolidation tasks for you. They also have the advantage of being easy to use and don’t require a data engineer to operate.

When selecting a data consolidation solution, make sure to choose one that can handle all your business needs. Look for a solution that can consolidate all your data types, offers a complete set of business analytics tools, and provides easy integration with other data analytics solutions. If possible, select a solution that can deliver predictive analytics and machine learning so you can get more value from your data. You should also consider integrating it with your existing enterprise systems so that you don’t have to change your existing technology infrastructure.

Self-Service Analytics

If your data analytics solutions offer self-service capabilities, users can explore, manipulate, and evaluate data without waiting for a data analyst to produce or explain it for them. This helps to improve data literacy and create a culture of data-driven decision making.

However, this only works if your users can use the tools easily. Traditional analytics tools can be complex and confusing, with steep learning curves, SQL queries, and complicated syntax. The best way to ease the burden on your analysts is to make it easy for business users to use the tools, which is why modern self-service analytics solutions offer natural language search, conversational interfaces, and visual data stories.

The goal of these augmented analytics tools is to allow business users to get the insights they need, when they need them. This reduces dependency on centralized analysts and allows your organization to become more insight-driven, which leads to faster, better decisions, improved outcomes, reduced risk, and increased customer satisfaction.

In addition to the obvious benefits of reducing the burden on your analysts, giving your business users access to self-service analytics also helps you to streamline operational procedures, enhance collaboration and communication, and increase productivity across departments. However, to ensure that you are delivering value to your users, it is essential to choose the right tools as per your requirements and train them well in advance to handle and run analytics independently.

Unlike legacy business intelligence solutions that require data specialists to manually build reports for every stakeholder, self-service analytics allows you to centralize your data on a single platform so that stakeholders can use a no-code analytics interface to visualize and analyze their own data. This eliminates the need to switch between tools, export spreadsheets and CSV files, run SQL queries, and deal with the inherent data quality risks that come with these processes.

In order for self-service analytics to be effective, your data team must first cleanse your data and ensure that only accurate information is available for reporting. Additionally, a robust governance framework must be in place to control the use of self-service data sets to prevent inconsistent data and potential security risks.

Real-Time Analytics

As the number of connected devices grows, businesses face an ever-increasing volume of real-time data. They’re collecting data from customers, operational infrastructure and machines, and they want to analyze it as soon as possible without leaving their applications.

Using real-time analytics is critical for a wide variety of use cases, including driving customer satisfaction, improving decision-making and detecting threats and problems before they become serious issues. In many cases, the most value comes from combining real-time and historical data to find patterns that can then be used to make predictions about future behavior and outcomes.

The best real-time analytics solutions reduce the time it takes for users to get the insights they need. They eliminate the need for lengthy ETL (extract, transform and load) processes or custom-built pipelines to converge data into usable form. Instead, they provide low-latency analytics through a single database that allows you to perform aggregation queries in place.

This type of technology makes it possible for a business to react quickly to market trends, user behavior and production performance. It also allows them to improve their bottom line by reducing downtime, improving productivity and increasing customer satisfaction.

A good example of a real-time analytics application is a customer support system that monitors calls to identify when a customer is at risk of churning and offers immediate in-call guidance for the agent to correct the situation. Other examples include real-time predictive maintenance that automatically sends alerts for potential equipment failure or identifying a recurring problem before it causes a major disruption.

Real-time analytics can also be used to enhance a marketing campaign, drive personalization for online customers or boost the relationship between sales and marketing teams. It can also help to mitigate risks by monitoring security breaches, fraud and operational performance.

An ideal data analytics solution is designed to ingest all available data, irrespective of source and location. It combines structured and unstructured data, internal and external sources, onsite and in the cloud to enable businesses to build dynamic new experiences and drive better decision-making. It also streamlines every step of the analytics process to deliver results in real-time without slowing down your business.