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Business Intelligence best practices - Modern Data Architecture
Business Intelligence best practices - Modern Data Architecture

In a world obsessed with Data, exercising some of the Business Intelligence best practices would determine whether you are harvesting fruitful crop or weed.

It is safe to say (nearly) every business is a software company in the 21st century. SaaS adoption is occupying one of the biggest chunks in the pie chart of IT expenditure. Now for creating products/services, to market them, to sell them, to manage the supply chain, to communicate, to buy, to sell, to track, and for everything else in between – There is a SaaS for it. As the product architectures are evolving with time and customer needs, the modern data architectures are ought to live up to the swiftly changing market conditions and need for ‘proactivity’ in production, supply chain management, marketing, and customer experience.

The journey from Data Warehouses to Data Platforms

A few years ago, the businesses that realized the growing importance of data started storing all the information, dedicated a team, invested in IT infrastructure for it, and called it Data Warehouse. As the underlying technology started wearing off, soon the tech leaders realized that there is minimal ROI on these corporate data dumps unless we start to reinvent things. But again, it’ll be ungrateful to underappreciate these Data Warehouses since they laid the first steps towards Business Intelligence.

Soon after the Data scientists changed the order of data computing approaches from Extract – Transform – Load (ETL) to Extract – Load – Transform (ELT), which expanded the scope of the concept from merely storing data for basic integrations, reporting, and analysis for specific business functions. The emergence of Data Platforms allowed businesses to store unstructured raw data and created a data environment that is flexible, adaptable, and agile.

Our last article ‘Why Should Business Executives Back Data Lake Investments?’ gives more insight on the subject.

The Modern Data Architecture to enable Business Intelligence


As we always say, Qentelli believes in personalization and innovation. This Modern Data Architecture is only a broad template to depict the direction of data flow and order of actions that are involved in the process of acquiring Business Intelligence. Whenever we embark on a journey of data transformation for any client, we slightly adjust the components based on the business goals and data capabilities. Elements that don’t serve any purpose will be removed and the terminology will be changed according to the business's internal processes and communications.

An ideal Modern Data Architecture that is built with an objective to achieve reliable Business Intelligence, must have the following characteristics.

  • Customer-centric – Consider all your data investments futile if the architecture is incapable of evolving to meet the changing (internal and external) customer needs.
  • Adaptable – The whole purpose of having centralized data platforms is to have the convenience to build interconnected yet bidirectional data channels to serve various business needs. Each data object is expected to act as a reusable and replenishable building block.
  • Automated – Modern data science involves regular tagging, mapping, detections, alert triggers, and a lot of repetitive tasks. Handling all of them manually isn’t very smart.
  • Smart – Speaking of smart, it is high time to involve AI in data architectures wherever we see fit. Ideal modern data architecture can learn, adjust, alert, and recommend as and when needed.
  • Collaborative – Data isn’t just the IT team’s responsibility anymore. Every business unit must contribute to strengthen the data environment and add business context right from the initial stages.
  • Governed – Data Governance helps teams to move from reactive to proactive. Compliance, regulation, and access controls bring in operational harmony and ease of business integration.
  • Simple – As the business scales, Data Architectures can sometimes become needlessly complex. Pair up with a technology partner who has experience in handling Digital Innovations and has a full-fledged team of data science experts.
  • Scalable – Not just Data Architecture, no org-wide transformation initiative is worth it if it can’t scale directly proportional to the variable workloads.
  • Secure – Encryption isn’t all. Mask every Personally Identifiable Information (PII), have an active data catalog to trace all the elements, including lineage, usage, and audit trail.
  • Resilient – We can’t be too careful in today’s all-connected world. Aim for high availability with good disaster recovery, backup, and restore capabilities.

Business Intelligence Best Practices

“A successful Business Intelligence strategy is not just about employing the strongest technology platform, storing the largest piles of data, or having the biggest team of data stewards.”

It is about catering to every need of internal as well as external customer’s data needs and that is only possible if you have a business-first and customer-first approach in your BI strategy.

  • Business Buy-in, Collaboration, and Ownership

    Aim for complete buy-in from the executive board and take the help of technology heads to explain the benefits and processes. Form an internal network of contributors to enrich the data environment continuously. This network should consist of personnel from every team including Marketing, Sales, Logistics, Finance, Delivery, Management, and Customer Service. The business units must not depend on IT to generate reports and insight.

  • BI Implementation Strategy

    A couple of years ago, a Forbes article by a fintech leader predicted that more than 150 zettabytes of real-time data will need analysis by 2025. The complete potential of data is yet to be discovered. We are dealing with massive loads of data and without a practical Business Intelligence implementation strategy, your ROI might not live up to the TCO (Total Cost of Ownership).

  • Monitor, Learn and Adjust the process

    Unless you have a seasoned team of Data Scientists, you will most likely find some roadblocks at the initial phase of your Data Management and Business Intelligence creation. Don’t let a few hiccups dull your motive, rather take the IT team's help to establish continuous monitoring systems, access your data sources, understand the underlying conditions, and adjust the elements to align your BI strategy to your business goals.

  • Data Validation for Certainty and Accuracy

    A ‘Vote in’ doesn’t directly imply trust over the process. A few business units, their leaders, or some of the key contributors may have a lot of questions about how accurate are the insights that are being produced from the so-called Business Intelligence practices. Organizations need strong Data Validation processes in place to gain true trust from every business user. Design the validation process to be able to detect and prevent problematic data, avert faulty insights, and add agility.

  • Business Challenge comes first and then the Query

    Just because you’ve built a data repository don’t expect the business users to dive right in with queries. Business Intelligence sure is a magic wand that can bring you insight with few clicks but step one is always dissecting the business challenge. First, have a clear list of metrics you want to track and analyze, and then place your query for Insights.

  • Design your Business Intelligence dashboard informative and impactful

    The BI dashboard is one of the most important aspects of your BI reporting. Data Visualization is not only for aesthetic purposes, but also to add simplicity, readability, clarity, and let the users effortlessly find the information. Opt for a dashboard design that presents detailed analysis as needed in a downward flow. Modern Data Architectures emphasize regulations and access control. Take advantage of the access information to display the relevant KPIs and Metrics at the top of each log-in.

  • Upskill the business users

    The scarcity of top-notch data scientists is real. But you can turn the most inquisitive employees of your workforce into information analysts. Because they already know the business domain, understand their respective business units, and know what questions to ask. All they need is some training to handle the platforms and unmodeled data sets. This could be your first step towards self-service Business Intelligence.

In a broader sense, BI is a combination of Data Collection, Storage, and Information Management. It requires a cross-functional team of data architects, data engineers, data analysts, and other data management professionals. Some of them may not be full-time data professionals. In small and medium-sized enterprises (SMEs), personnel from various teams will be formed as a team so they can present the business side and make the most of the BI platforms. Many market and business analysts are saying that these are the trends the BI space will see in the near future.

Technologies for Augmented Analytics where the user can post queries in natural language instead of SQL or any other programming language.

Low-code and No-code environments. Most of the leading BI vendors are adding interactive and user-friendly graphical interfaces that let the users access data and create insights with little to no coding knowledge.

Cloud storage might become more common since modern data warehouses aren’t on-premise physical infrastructures. The current circumstances are encouraging cloud-based data storage as well as cloud-based BI tools and platforms.

The businesses will invest more in enterprise-wide data literacy. As the premise of data collection and consumption is broadening day by day, BI becomes relevant to every business unit. Slowly but surely the management would want more teams to have access to Business Intelligence to perform better and innovate faster.

Building modern data architectures won’t guarantee success in BI. Just like many other business transformation adoptions, Business Intelligence is a continuous process and needs to evolve directly proportional to the growing business needs. Infusing the right amount of Artificial Intelligence and Machine Learning capabilities at right time can enrich the quality and accuracy of BI churned. We hope these Business Intelligence best practices help you find your competitive edge and acquire deeper insights that can nurture your enterprise. But if you find it complex and feel you need a little more help, simply drop us an email. Our team of experts loves listening to business challenges and suggesting sustainable and scalable solutions.