BI systems have become a must for every modern company wanting to efficiently collect, process, and study data in all dimensions, from descriptive to predictive and prescriptive analytics. Quality BI insights hugely invigorate a business in multiple aspects: improve decision-making, highlight trends, streamline processes, mitigate risks, and so forth.
Then, the challenges arise. Firstly, the more data influx an org has, the more sophisticated and capacious business intelligence infrastructure it needs. Secondly, adopting new BI implementations and business processes is unlikely to be frictionless. That's why organizations find integrating advanced BI solutions quite tricky.
Below, we’ll talk through building business intelligence configurations step-by-step, elucidate some crucial nuances, and draw your attention to typical challenges, as well as ways to surmount them.
Table of Content
1. The Benefits of Successful Business Intelligence Implementation
Everybody knows data is one of the most valuable business assets. Nevertheless, since embracing business intelligence solutions rarely goes smoothly, some may doubt whether it's worth their while in the first place. Well, it definitely is. Before delving into the intricacies of a BI strategy application, let's recall how advantageous it is for businesses to utilize such systems.
No Data Wasted
First and foremost, a dependable BI system captures data from numerous touchpoints and amalgamates it into one centralized system. Data consolidation and harmonization is an essential prerequisite for efficient data processing. And without a single source of truth, we face the data silos issue and can't see a holistic picture of how the business is doing, how customers react to marketing activities, and so on.
Besides, a powerful, AI-enabled BI system substantially improves data accessibility and visibility, helping us obtain insights, reports, and forecasts expeditiously. Instead of waiting for reports for days or even weeks and reacting to changes, we can configure real-time data tracking and be more proactive.
Farsighted Decision-Making
Modern business intelligence tools can be not only all-encompassing (accumulating and handling all meaningful data) but also user-friendly, with customizable dashboards and convenient reporting/data visualization features. Thus, smarter data-informed decisions become possible at all levels, from executives to department heads to managers, sales reps, marketers, and customer service agents.
Plus, quality BI facilitates data-driven decision-making in all directions a company finds important. Data insights sharpen your wits in demand forecasting, inventory management, pricing, marketing strategies, staff management, etc.
Profound Customer Insights
Your website, socials, targeted ads, email campaigns, and other channels generate enormous raw data volumes. Frankly, without a BI implementation, these are largely untapped resources. Scattered analytics tools will cover some parts, and employees will cherry-pick insights, but the org won't have a full-fledged picture to scrutinize.
With business data constantly processed by a BI system, we obtain solid clues on leads' and customers' behavior throughout the sales pipeline. These insights are a surefire way to better understand your audience: who are these people, what they prefer, need, and don't want. Then, you can level up personalization and customer experience. And, of course, improve major KPIs like engagement, conversions, churn rate, average order value, customer retention, and others.
More Accurate Forecasting
As AI is picking up steam, predictive analytics is getting more precise and actionable. After implementing AI-powered BI tools, you'll be able to forecast future market trends, risks, and scenarios far more accurately than ever before.
Correct prognoses are as helpful for routine tasks (like predicting the monthly demand for product X) as for global business tasks (like adjusting the overall company development strategy and exploring new revenue opportunities).
Perfectly Optimized Processes
Consistent business intelligence usage leads to higher operational efficiency. Data analysis aids in optimizing business processes, getting rid of inefficient practices, and doubling down on fruitful ones.
In some cases, a so-called self-service analytics system can even substitute in-house data analysts and deliver actionable insights without the help of middlemen. As a result, you can also expect workflow acceleration, an increase in team productivity, and a decrease in operational costs.
So, whether your business growth is stalling, you plan to scale up, or wisely choose not to be complacent about the company's current state, it makes sense to ruminate over introducing/upgrading a business intelligence solution.
2. Ten BI Implementation Steps
When implementing BI, you don't want to act haphazardly, risking ending up with a non-viable system. So, before commencing BI development, during the process, and after the active dev phase is over, there are some crucial steps to follow.
Here is Onilab's 10-step business intelligence implementation plan. The stages may differ depending on who will perform the tasks and what kind of BI you need, but typically, a business intelligence implementation roadmap looks somewhat like this.
01. Assess the Current BI Configuration
Start by wading through and documenting your data processing environment as of now to form a more efficient business intelligence strategy down the line. The main focal points are:
- Data types and sources. Examine what data you collect and analyze and how. Which sources of data do you prioritize, and which overlook, and why?
- Data management. Explore how data handling is approached, from aggregating raw data to data mining to making data-driven decisions. It helps detect underperforming spots in such pipelines.
- BI tools in use. What about your current BI software, has it proven effective so far? Are basic and predictive analytics insights really helping, are dashboards and reports convenient for the teams?
- People in charge. What do employees accountable for different data processing phases think about the current business intelligence implementation? Ask what flaws and deficiencies they notice in day-to-day BI tasks.
Who should be in charge of this task? Companies can carry out a thorough evaluation themselves if they have data experts, do preliminary research without technicalities if they don't have specialists, or gather an outsourced team right off the bat to conduct a full BI audit.
Get a BI Dashboard Optimization Template
Maximize your use of BI dashboards. Discover hidden opportunities to capitalize on the tool’s capabilities.
02. Define the Goals and KPIs
The next vital step in a successful BI implementation journey is to determine what business needs, processes, goals, and pain points a business intelligence system is supposed to help you with. While BI isn't a magic bullet per se, it can be very potent if rightly targeted and configured.
The use cases are plentiful. For instance, an eCommerce brand may need a powerful BI platform to reinforce its shopping personalization, i.e., product recommendations as well as up-sell and cross-sell blocks. A medical center might want to level up forecasting accuracy to further decrease the number of missed doctor appointments. An IoT solutions seller might aim to improve customer segmentation and predict demand better.
Together with picking major BI use cases, it's essential to highlight corresponding key performance indicators. Concrete metrics allow us to focus on the main objectives when developing the platform and measure the effectiveness of the BI project implementation over time.
03. Gather the BI Implementation Team
A couple of thoughts on appointing the business intelligence implementation team. Basically, you have two paths: gather an in-house BI project crew or hire an outsourced team.
The first option is possible only if the org has all the needed staff on payroll, including a BI consultant, PM, BA, data scientist, and BI developer. Otherwise, a company has to hire new people, which is likely to take extra time. It's more a path for large businesses wanting total control over the process and the highest level of data protection.
The second way involves handing the project over to a business intelligence implementation agency like Onilab. Since such third-party vendors specialize in developing and deploying BI solutions, their clients enjoy faster time to market and cost savings. The only caveat is that you need to choose an outsourced BI team carefully.
04. Create the BI System Concept
Then, it's time for a BI solution architect and company stakeholders to ruminate over which kind of BI software best suits a particular business. There are several business intelligence architecture types we can choose from and combine to design a proper system for a client.
Under the hood, each BI solution has pretty much the same structure: data sources, data integration and data quality management tools, data storage (data warehouse, data lake, data mart technologies), data analysis applications, interactive dashboards, and a secure data governance layer.
The difference lies in the level of a BI system's complexity: its capacity, customizability, and capabilities. Based on this criterion, we roughly single out four categories of BI software. Let's see what's on the table.
Ready-made BI. These are pre-built platforms to be quite quickly deployed and customized to some extent to cater to different use cases. Out-of-the-box tools are optimal for SMBs prioritizing an easier business intelligence implementation process and budget savings while being content with more or less basic BI features. The most popular platforms of this sort are , , , and .
Custom BI. These are tailor-made BI platforms created either based on ready-made platforms or completely from scratch to fit unique org's requirements. In this case, BI implementation involves extensive custom development and multiple integrations to bring new features and facilitate company-specific processes. Let's say you're a logistics giant with complex supply chain management tasks (inventory management, shipment tracking, etc.). You'll definitely need robust big data processing and predictive analytics capabilities.
Hybrid BI. It's a match for cases when standard functionality is largely enough for a business, but still, there are some features aren't covered by an out-of-the-box BI tool of choice. Plus, additional integrations with custom or legacy systems might be needed. Then, a BI team can pick a proper solution and add extra customizations. It's a go-to option for a large proportion of businesses, especially those operating for quite a long time and having some custom-made software (like a CRM or ERP) they want to connect with new BI tools.
Self-service BI. These tools offer a user-friendly interface, drag-and-drop dashboard customizations, instant access to insights, and comprehensible querying and reporting features so that people with little knowledge of data analytics can utilize them. Many ready-made BI platforms (like , , and ) can also be used as self-service ones. For instance, a digital marketing agency with more common data tasks and without in-house data analysts will greatly benefit from a self-service BI implementation.
05. Choose the BI Tech Stack
At this stage, a business intelligence team opts for the core (either one of the existing BI solutions or the tech stack to build one from the ground up); decides on infrastructure and auxiliary tools to deploy the platform; selects the hosting type (on-premises, cloud, or hybrid).
Successful BI implementation relies on several factors, with software choices being one of the central ones. The system will unite multiple channels and tools from different universes: data sources, ETL software, data repositories, and tools to analyze data (from descriptive and statistical analysis to predictive and prescriptive analysis), visualize it, create reports, and ensure data security. Therefore, a part of a BI strategy is to solve this puzzle and get a perfectly compatible and manageable configuration.
06. Develop the BI Solution
As soon as the team has determined business goals, business intelligence implementation strategy, and the right BI implementation tools, the development can begin. Let's see what stages this process typically contains.
- Data preparation: identifying data sources and developing ETL (Extract, Transform, Load) pipelines to get, cleanse, and move data to storage.
- Data storage setup: configuring the data warehouse, data marts, and data lakes to store all data needed for analytical purposes.
- Data integration: connecting all the parts of your BI environment, including existing systems, data storage, new tools, etc.
- Data management: implementing data governance (cleansing, validation, security rules and policies) to eradicate poor data quality affecting BI performance.
- BI customization: configuring the BI platform and adding custom business logic to expand the system's functionality.
- UI, dashboards, and reports creation: developing handy interactive dashboards and reports for clear data visualization and presentation.
- QA and optimization: testing BI environment parts' compatibility, data extraction and subsequent processing quality, calculations and predictions accuracy, performance, and usability.
07. Test the BI System
Prior to company-wide business intelligence implementation, it's recommended to deploy it on a small scale, e.g., for one department or focus group. The purpose is to try out the system live while mitigating risks of disrupted processes if something goes awry. Such a test drive isn't an obligatory step in a BI implementation strategy, but it increases the odds of a smooth and seamless big launch.
08. Gather Stakeholders' Feedback
Those employees participating in the BI testing are a great source of insights key to successful implementation in all departments. You can create a feedback loop by monitoring the focus group's performance, interviewing members, and utilizing this data to further adjust and refine all data-related operations.
09. Educate End Users
Oftentimes, employees meet novelties half-heartedly, especially when grappling with them on their own. That's why championing a data-informed culture isn't enough. Having a user adoption strategy with a proper training program is an extremely important step toward successful business intelligence implementation.
Otherwise, many may find the BI solution inconvenient and too complex. Others will hardly tap into its massive capabilities, simply not being aware of them. And some will be befuddled and disappointed by mistakes creeping in reports because of insufficient BI tools knowledge.
10. Deploy the Full-Scale BI Platform
The final point of our business intelligence implementation guide is going live company-wide. In large enterprises, where each department needs certain capabilities, dashboard customizations, and data security policies, it makes sense to deploy the BI system in several iterations.
Be ready to monitor the BI solution and troubleshoot any performance and data quality issues for the first weeks after launch. Then, you can start to assess how the new business intelligence strategy impacts key performance indicators.
Business Intelligence Implementation Project: Planning is Everything
To not only keep up with competitors but considerably hoist your KPIs in the 2020s, implementing business intelligence is a necessity. Creating the BI strategy, development, and deployment gives orgs a lot of hassle, but businesses tend to recoup their initial costs and actively use advanced analytics systems if their BI projects are curated by pros. Order BI consulting and from Onilab to guarantee successful implementation.