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The Current State of Analytics and Business Intelligence in 2022

    Analytics and Business intelligence (BI) has become an indispensable asset to organizations, big and small. Everyone wants to leverage every bit of available information and extrapolate possible future results to make informed decisions. 

    So you need to know all that is happening within data, analytics & business intelligence (BI) in 2022 to stay abreast of the latest industry developments for future business success.

    Providing analysis and insights instead of just historical data to interpret

    Most modern business intelligence is focused on what happened in the past. The system takes data from the last quarter, last year, or whenever, and it will tell you what happened. Past-tense analysis can be useful, but it is no longer enough. Forward-thinking companies need BI systems that can answer why something happened, make suggestions about what to do, and predict what may happen in the future.

    How DotNet Report Builder Helps?

    If you are going to benefit from these types of insights, you need analytics and business intelligence systems that can do more than just crunch numbers. This is one of the reasons why experts from Gartner and elsewhere claim that so many businesses are looking to augmented analytics as a solution for business intelligence. BI platforms that use augmented analytics can take data from several sources, identify trends and connect the dots to reveal causation. Many of them even employ technologies like predictive and prescriptive analytics to forecast future events and deliver solutions.

    Analytics and Business intelligence data were not available in the same place. With the help of modern BI tools like DotNet Report Builder, data is made available in such a manner that users can take immediate action. The BI tools allow you to combine processes and workflows with the help of extensions, APIs, and embedded analytics. Thanks to the combined technologies, it becomes easy to implement actionable analytics.  

    Users can view the data, derive actionable insights, and implement them in a single place. By using modern BI tools, you can even see where your users are coming from. Actionable analytics helps you create insights specifically for a particular department, function, or region.

    Individually curated insights

    Another common problem with many BI tools is that they offer a one-size-fits-all experience. These tools can still offer value, but we all know that different users have different needs. If a business is going to leverage data to its full potential, it will need BI tools that are flexible enough to offer individualized experiences for different users.

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    Having the right BI tools will be part of keeping up with this trend. You will need to look for platforms that not only can curate insights based on the needs of the user but ones that are designed to be used by people with different levels of technical skill.

    But it isn’t even just about the tools. Leaders will also need to take steps to encourage the adoption of advanced analytics. That means making sure employees know how to use the available BI tools and that they understand the ways these tools can improve their output. It’s good to have the right tools, but you won’t get the full benefit of BI if people are not using them.

    Improved accessibility and ease of use

    Under conventional methods for BI, people would send queries to the analytics team, and they would create reports and provide feedback. You might have a standard dashboard that provides some relevant data, but you had to run it through the analytics team if you needed anything beyond that. This would create bottlenecks and make it so the insights were less accessible to the average user.

    How DotNet Report Builder Helps?

    One of the biggest shifts in BI is to address this issue by bringing the average user closer to the insights. BI systems are now starting to deploy Natural Language Processing to make the insights more accessible. With NLP, users can query the system by asking questions in natural language. This is almost like using a search engine for your data. These BI tools can even generate reports without the need for an analytics professional.

    This is another example where having the right tools is only a good start. Business leaders need to show users how beneficial the tools can be and how easy they are to use. When users see they can search data with keywords or ask simple questions without the need for any expertise, they will be excited to adopt a tool that will provide the insights they need without having to go to someone else to get the answers.

    Ubiquity through mobile BI

    With shifting work patterns and business demands, companies worldwide are looking to equip their remote workers with productivity-enhancing software. These include SaaS solutions for access to reports and dashboards at any time, anywhere.

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    While most internet users access the internet via mobile, market share isn’t the only reason to implement mobile BI. Users can opt to receive KPI-based alerts and react promptly to events as they happen. Mobile BI features a responsive, lite version of the main analytics platform, placing the power of insights directly in the user’s hands wherever they go.

    BI users no longer need to be stuck to a desk when they need insights. Every user at your company uses a mobile phone and they might do business when they are away from the office. In the modern business environment, you might even have users that do some or all of their work away from the office.

    This on-demand availability of information enables faster decision-making, shorter workflows, and more effective internal communication. Though limited screen size and functionality can be restrictive, mobile analytics is a game-changing trend in business intelligence. Mobility is part of many software vendors’ offerings in response to companies’ BI requirements lists for 2022.

    Self-Service Analytics

    Big data analysis is an intricate process that requires the substantial involvement of professional data scientists. Fortunately, with the advent of self-service BI (SSBI), the approach to data analytics is changing fast.

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    Self-service business intelligence has been on businesses’ wishlists for a long time. Business users were not comfortable with the complexity of rigid BI analytics tools. Besides, the need to bring data scientists to handle the analytics was inflating operation costs. Consequently, this induced the perpetual craving for self-service and flexibility in analysis and reporting.

    Hence, self-service business intelligence was born. This technology has proven to be a timely intervention. Statistics show that self-service BI is still a top priority for many businesses (BARC, 2021). The service enables business users to handle BI tasks on their own without involving data scientists or IT teams. So, it empowers users to filter, sort, and analyze corporate data without necessarily having technical data analytic skills.

    Interestingly, it is predicted that going forward; self-service BI will produce more analytics than data scientists. This points to the growing importance of self-service BI. With more and more businesses planning to use BI to promote data-driven culture, the self-service business intelligence trend will only gain more traction.

    Modernized Data Warehouses

    A data warehouse is a data management system that stores large amounts of data for later use in processing and analysis. That data is then sorted into rows and rows of well-organized shelves that make it easy to find exactly what you’re looking for later.

    How DotNet Report Builder Helps?

    In business intelligence, data warehouses serve as the backbone of data storage. Business intelligence relies on complex queries and comparing multiple sets of data to inform everything from everyday decisions to organization-wide shifts in focus.

    To facilitate this, analytics and business intelligence is comprised of three overarching activities: data wrangling, data storage, and data analysis. Data wrangling is usually facilitated by extract, transform and load (ETL) technologies, whereas data analysis is done using business intelligence tools.

    The glue holding this process together is data warehouses, which serve as the facilitator of data storage using online analytical processing. They integrate, summarize, and transform data, making it easier to analyze.

    Data scattered across various sources and in differing formats limits leadership’s ability to measure the business and make well-informed decisions. With the data integration that data warehouses provide, users can leverage all of the organization’s data for more accurate insights. A data warehouse provides improved data quality, as it cleanses, eliminates redundancies, and standardizes the data to create a single “version of the truth.”

    Real-time analytics

    Real-time business intelligence is the use of analytics to deliver instant and highly actionable insights. It gives companies access to the most recent, relevant data and visualizations. More than anything, this latest information lets organizations make smarter decisions and better understand their operations. To provide real-time data, these platforms use smart data storage solutions.

    How DotNet Report Builder Helps?

    At its core, real-time business intelligence is about understanding data faster and using it to make wise snap decisions. For organizations that produce gigabytes—and even terabytes, in some cases—much of this information loses its relevance once it’s sitting in storage. Information about inventory levels, customer needs, ongoing services, and more can all be incredibly useful, but more so if it’s analyzed as soon as it’s generated.

    Real-time analytics and BI also empower users in organizations to perform their research and use their available data. This includes the ability to perform ad-hoc analysis on existing data or create visualizations specific to new streams. Finally, real-time BI helps better understand trends and create more accurate predictive models for organizations.

    Data preparation

    Being able to prep your data is a very basic component for achieving good results with BI. Data prep is about the process of cleaning, structuring, and enriching data for use in the analysis. The goal of data prep is to turn raw data into valuable insights that can be used to answer specific business questions. One of the primary purposes of data preparation is to ensure that raw data being readied for processing and analysis is accurate and consistent so the results of BI and analytics applications will be valid.

    How DotNet Report Builder Helps?

    BI and data management teams use the data preparation process to curate data sets for business users to analyze. Doing so helps streamline and guide self-service BI applications for business analysts, executives, and workers. 

    Data scientists often complain that they spend most of their time gathering, cleansing, and structuring data instead of analyzing it. A big benefit of an effective data preparation process is that they and other end users can focus more on data mining and data analysis which would generate business value.

    Effective data preparation is particularly beneficial in big data environments that store a combination of structured, semi-structured, and unstructured data, often in raw form until it’s needed for specific analytics uses. Those uses include predictive analytics, machine learning, and other forms of advanced analytics that typically involve large amounts of data to prepare.

    Data Governance

    Data governance is a process that stipulates the blueprints for managing corporate data assets, including process, operational infrastructure, and architecture. Put simply; it forms the sturdy foundation upon which organization-wide data management happens. In contrast to BI or analysis management, which is about preparing and presenting data for business management systems, data governance focuses on the actual data in these systems.

    How DotNet Report Builder Helps?

    Data governance is required to implement a data strategy and contains rules and frameworks for managing, monitoring, and protecting data. This is done while taking into account people, processes, and technology. It enables companies to harness the power of technologies, processes, and people involved in the management of data assets to deliver complete, trustworthy, secure, and understandable data.

    Data governance ensures the quality of business assets through role-based access, authentication protocols, and auditing. When data is accurate, unique, and up-to-date, users trust the insights are reliable, boosting revenue and reputation. Establishing data governance is a long-term endeavor. First and foremost, it requires a clear and conscious management decision on how to work with, and use, data. 

    The General Data Protection Regulation (GDPR), designed to protect individual personal data, falls under the scope of data governance. With different regions adopting GDPR policies, having a data governance system will be essential to every business. In addition, modern BI tools are supporting data governance models to maximize the value of analytics.

    Let us know if there are any questions or comments.

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