Data analytics tools enable organizations to achieve their desired technical and business outcomes. They’re used by financial institutions to predict market movements and inform investment strategies. In healthcare, they’re used for patient outcomes predictions, resource management and personalization.
But analyzing this data can be challenging due to its fragmented nature and inconsistent formats. A good data analytics solution addresses these issues by:
1. Automated
As companies grow, it becomes too time consuming and complex for data analytics teams to manage manual processes like data collection, data preparation, and data classification. Automating these processes ensures quicker and accurate outcomes without any human activity, and helps to avoid errors that may result from manual workflows. Additionally, automated data analytics solutions help to reduce the risk of data leaks and ensures that only trusted personnel have access to sensitive information.
However, it is important to note that automation cannot replace human intelligence in every instance. Some tasks like asking questions, validating statistical models or translating numbers and graphs into actionable insights, cannot or should not be automated. It is important for organizations to identify which parts of their analytics pipeline can be automated and understand that when they do choose to automate, they should plan carefully to minimize disruptions to existing analyses and avoid introducing new errors into the data pipeline.
When implementing an automated data analytics solution, it is essential to determine metrics for measuring performance and utility so that teams can track the success of the process. These measures are also useful in identifying the potential for future optimizations or extensions to the automated system. Once a set of criteria has been identified, it is important to create and test the automated process, then iterate to refine it. It is also important to remember that the automated system should be designed for efficiency, and should not add more steps or effort than a manual workflow.
Segment provides end-to-end data automation solutions for tens of thousands of businesses, allowing them to connect with any source or destination and automatically apply QA checks for data quality and protect data at scale with automatic tagging or masking. This helps to ensure that only relevant and accurate data is analyzed for decision making, while ensuring that any data used in an automated manner is compliant with privacy regulations.
Cloud platforms that host data warehouses often provide tools for automation, such as Google Analytics’ built-in Analytics Intelligence tool which utilizes machine learning to flag anomalies in time series data at the click of a button. Alteryx, for example, democratizes self-service analytics with an integrated, automated and unified experience, providing citizen data scientists the ability to skillfully combine, prepare and analyze all kinds of data, from on-premises systems to disparate data sources, seamlessly connecting both cloud and on-premises workflows and data analytics processes.
2. Consolidated
Data is valuable on its own, but it becomes even more useful when combined with other data. Human resource systems, sales systems, product databases and hundreds of other business applications contain data that can be consolidated into a single source to provide the insights that lead to better decisions.
To accomplish this, the data must be unified in structure and format and moved into a central storage solution like a data warehouse or data lake. This process is called data consolidation or integration and involves mapping, transformations, and the removal of duplicated information. There are a variety of methods to consolidate data that range from the straightforward (using software tools to merge multiple files into one) to complex (integrating systems with millions of interfaces that must be mapped and consolidated).
In the mortgage industry, it’s important to have a single source for all your loan data. This will allow you to see all the information you need in one place and make better informed decisions about your loan products, processes, and customer experiences. It will also help you identify areas of improvement. For example, you might find that a particular marketing channel isn’t performing well and needs to be revamped.
Consolidated data can also improve your customer experience by allowing you to maintain consistency across departments and platforms. Customers might begin a transaction on your website and finish it on a mobile app, and they expect the same experience. Data consolidation helps you keep this consistent so that the customer can easily move from one platform to another without having to start over again.
There are two main ways to consolidate your data: hand-coding or using software tools. The latter is a quicker and easier option, especially when you’re working with smaller amounts of data. It’s also more cost-effective compared to hand-coding, especially if you have a lot of data sources. You can choose from a variety of software tools that use ETL, data virtualization or data warehousing for data consolidation. Some of them are no-code solutions that work on cloud computing models.
3. Self-service
A key element in the modern data analytics sphere, self-service empowers business users to explore, analyze and make decisions on their own. It’s a facet of modern BI that encourages self-reliance, and when used effectively, is essential for organizations that want to democratize access to data and insights for all business stakeholders.
It saves time and effort when stakeholders no longer need to submit requests to IT or analysts for departmental metrics and reports. Instead, they can use a no-code platform to view dashboards, generate reports and slide and dice data themselves to get reliable answers quickly. This allows them to make high-impact decisions such as where to invest profits, when to launch a new product, or which markets to enter that require a thorough examination of facts and numbers, comparisons to past events, and correlations with influencing factors.
This translates into significant cost savings and productivity benefits for the organization as a whole, including faster product launches and higher data-driven performance metrics across multiple departments. Additionally, when IT teams don’t have to spend time fielding requests for reports from business stakeholders, they can focus on other high-value technical projects that drive data innovation.
To maximize the benefits of self-service data analytics, a solid business intelligence solution needs to offer a streamlined, user-friendly interface. The best options feature advanced AI, machine learning and natural language technologies that can interpret business questions, provide relevant visualizations and reports, and automatically complete repetitive tasks such as creating charts, tables and pivot tables. Moreover, they also offer the flexibility for business users to easily customize dashboards and reports with the information that is most relevant to them.
Finally, a self-service analytics solution should allow users to share insights through their favorite tools like Google Sheets, Microsoft Teams and Slack so that they can collaborate with colleagues more efficiently. The ability to easily share and push insights to other platforms helps to foster collaboration between team members, and ensures that the right people have timely access to the data they need. In addition, the scalability of a self-service analytics solution is important as it should be able to handle increasing volumes of users and data seamlessly.
4. Scalable
Scalability is an essential factor in data analytics, enabling businesses to handle growing volumes of data smoothly and efficiently. This capacity ensures that as the business grows, the data analytics system will grow with it, providing valuable insights that help drive growth. In addition, a scalable data analytics solution allows for easy integration with other systems without affecting performance or requiring major adjustments.
Scalable analytics solutions are critical for businesses looking to keep up with fast-changing market conditions, customer needs, and emerging opportunities. These solutions allow businesses to analyze large volumes of data quickly and effectively, enabling them to make informed decisions and remain competitive.
The benefits of scalable data analytics include cost efficiency, improved productivity, and increased customer satisfaction. With a scalable data analytics solution, businesses can grow their user base and analytical capabilities without increasing costs or compromising on data quality. Additionally, a scalable data analytics solution can integrate with other systems, such as communication tools like Slack and MS Teams, making it easier for employees to access the information they need.
The key to a successful scalable data analytics system is a flexible architecture that can handle rising volumes of data without impacting efficiency or performance. Businesses should also focus on latency-sensitive metrics when designing a time-series data pipeline to ensure that data is transferred correctly and doesn’t cause disruptions in the overall analysis process. Finally, businesses should invest in a scalable data analytics solution that offers robust monitoring and alerting to identify potential issues before they become costly problems. By implementing these strategies, businesses can scale their data analytics solutions without losing sight of their goals and vision.