Business Intelligence Software Aesthetic – In a data-obsessed world, practicing some business intelligence best practices can determine whether you reap a fruitful crop or a weed.
A century. SaaS adoption occupies one of the largest segments of the IT spending pie chart. Now to create products/services, market them, sell them, manage the supply chain, communicate, buy, sell, track, and everything else in between – there’s SaaS for that. As product architectures evolve with time and customer needs, modern data architectures must scale up to rapidly changing market conditions and the need to be “proactive” in production, supply chain management, marketing, and customer experience.
Business Intelligence Software Aesthetic
A few years ago, companies that realized the increasing importance of data started storing all the information, allocated a team, invested in IT infrastructure for it, and called it Data Warehouse. As the underlying technology began to fade, technology leaders quickly realized that there was minimal return on investment on these corporate data dumps unless we started reinventing things. But again, it would be ungrateful to underestimate these data warehouses because they laid the first steps towards business intelligence.
Changing Software Vs Changing Business Process
Soon after, data scientists changed the order of data computing methods from extract-transform-load (ETL) to extract-load-transform (ELT), expanding the concept from simple data storage to basic integration, reporting, and analysis. For specific business functions. The emergence of data platforms has allowed companies to store raw, unstructured data and create a flexible, adaptable, and agile data environment.
As we always say, we believe in customization and innovation. This modern data architecture is nothing but a broad template for depicting the direction of data flow and the order of actions involved in the process of obtaining business intelligence. When we begin a client’s data transformation journey, we tweak the components slightly based on business goals and data capabilities. Items that serve no purpose will be removed and terminology will be changed in accordance with the company’s internal operations and communications.
An ideal modern data architecture created with the goal of achieving trusted business intelligence should have the following characteristics.
Business Intelligence Best Practices “A successful BI strategy is not just about employing the most powerful technology platform, storing the largest piles of data, or having the largest team of data stewards.”
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It’s about meeting both internal and external customer data needs, and this is only possible if you have a business-first and customer-first approach in your BI strategy.
In the broadest sense, business intelligence is a combination of data collection, storage, and information management. It takes a cross-functional team of data architects, data architects, data analysts, and other data management professionals. Some of them may not be full-time data specialists. In small and medium-sized enterprises (SMEs), employees from different teams will be formed as one so that they can deliver on the business side and make the most of BI platforms. Many market and business analysts say that these are the trends that the business intelligence field will witness in the near future.
Augmented analytics techniques where the user can post queries in natural language instead of SQL or any other programming language.
Low-code and no-code environments. Most leading BI vendors add interactive, easy-to-use graphical interfaces that allow users to access data and generate insights with little or no programming knowledge.
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Cloud storage may become more popular as modern data warehouses are not physical infrastructures within a company. Current conditions encourage cloud data storage as well as cloud-based business intelligence tools and platforms.
Companies will invest more in enterprise-level data literacy. As the scope of data collection and consumption expands day by day, AI becomes relevant to every business unit. Slowly but surely, management wants more teams to have access to business intelligence to perform better and innovate faster.
Building modern data architectures will not guarantee success in business intelligence. Just like many other business transformation adoptions, business intelligence is an ongoing process and needs to evolve in direct proportion to growing business needs. Instilling the right amount of AI and ML capabilities at the right time can enrich the quality and accuracy of business intelligence. We hope these business intelligence best practices help you find your competitive advantage and gain deeper insights that can strengthen your organization. But if you find it complicated and feel you need more help, just email us. Our team of experts loves to listen to business challenges and suggest sustainable and scalable solutions. info@Business Intelligence (BI) is an essential step for any organization looking to democratize data and unlock clear insights and visualizations from the various data sources in its business. Modern business intelligence tools easily sync with your data warehouse and create visualizations that make your data easy to understand when queried about it.
However, no two (or more) BI tools are alike. They differ on data democratization and sharing, intelligence reporting, last-mile modeling, and visualization capabilities. When faced with the task of choosing a BI tool for your company, here are some…
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Different business intelligence tools allow their users to create reports in different ways. When considering which BI tool is right for you, take a moment to think about your end users. What do their skill sets look like? Are they comfortable with SQL queries or do they prefer a drag-and-drop interface? Some organizations prefer a drag-and-drop interface for creating and formatting reports. This is a quick and convenient process, especially when your data is extracted correctly. However, it can fall short and become distorted if your data is not clean. This is one of the main reasons why technical staff prefer a more robust way of building reports, where they can use specific SQL queries for data mining purposes.
What is the scope of the BI tool you are looking at? Is it primarily focused on visuals, or is it a full stack offering that implements data modeling in the company’s warehouse? For example, a BI tool might feature its own modeling layers like Looker, its own collaboration features like Mode, or its own dashboard functionality like Periscope Data. Ask yourself whether you want to be in control of the initial stages of modeling, or whether you want to leave it to follow through technically via SQL. If the tool doesn’t have modeling layers beyond SQL, determine if you and your team can live without them.
Most BI tools provide user and role-based user security, specifying who can create, use, deploy, manage, or modify their applications. However, some organizations may want to integrate the BI tool with pre-existing security software or operating systems. Hence, check whether the Business Intelligence (BI) solution you prefer can use its security along with different mechanisms from databases, networks and operating systems. Simply put, the security of the solution must align with your current and future information security policies.
Your BI tool should not only offer flexible ways to present relevant information, but it should also have the ability to present data on any device. Since many decisions are now made on the go, the solution should include visualization options that can be viewed on any smartphone or tablet. Analyzing your data on a small screen should be a matter of clicking a few buttons, ensuring users get all the relevant visuals and reports, no matter where they are at the time. Most leading BI tools offer mobile-compatible BI apps that can be accessed from anywhere, but it’s worth checking that the tool you choose will be able to meet your accessibility needs.
Open Source Business Intelligence
When you’re considering using a business intelligence tool, ask yourself whether it’s easy to integrate existing dashboards and make them accessible to the right managers and users in your company. Most BI tools come with sharing, versioning, and permissions functionality. The size of these functions becomes more important the larger your company grows and the more you handle mission-critical data. Data democracy empowers all key stakeholders in your company to have the data and insights they need to elevate their performance and make well-supported decisions.
Does your company need more intuitive dashboards and visualizations, or do you place less value on data presentation and aesthetics? Some vendors may outperform others in certain aspects. For example, Tableau is widely known for its beautiful and powerful visualizations, while Domo is great for operational data at a granular level. Additionally, some tools will allow users to apply a filter to the data in the visualization, or drill down into small details to create higher-level reports.
Does your BI tool enable the creation of an internal community? Can members share and discuss their reports in real time? Large collaboration organizations will get value from a tool that allows users to share and reuse data with external departments. This may include analyzing annotations to share insights and social features, which can support chat threads and discussion. For example, the mode offers excellent collaboration and sharing features. Users can perform the same amount of analysis
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