Business intelligence implementation: 10 key steps 

Today, BI implementation is a key to business success. Long gone are the times when business owners acted on a hunch while running their enterprise. Today, even small locally-dealing firms prefer to collect information on clients’ demographics along with their tastes and preferences as to the products or services the vendor offers.  

For bigger organizations, collecting business data is a vital necessity that is highly instrumental in analyzing historical activities and the current stand of their venture, as well as in divining market trends symptomatic of their niche for months and even years to come.  

Purposing to outsell their rivals, blue-chip companies spend outrageous sums on similar activities, turning business intelligence (BI for short) into their major competitive differentiator.  

Is BI really that valuable? 

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Why is business intelligence important for your organization? 

The term “business intelligence” subsumes a whole range of procedures aimed at collecting, hoarding, and analyzing business operations information. This technology helps make knowledgeable data-driven decisions based on the comprehensive picture this overview creates. What activities does an average business intelligence implementation plan include? 

• Data drilling. Specific AI and machine-learning techniques are employed to discover and collect relevant data to trace trends in the obtained datasets. 
• Data arrangement. By amassing information from multiple data sources and aligning measurements and dimensions used there, the information is prepared for further analysis.  
• Benchmarking. On customized dashboards, historical data is compared against the current performance metrics to see how they dovetail into the preliminary set goals. 
• Querying. In the collected database, you can seek answers to very specific business-related questions.  
• Data representation. Charts, graphs, tables, and histograms can help visualize numbers to make them more “digestible” for further consumption. 
• Descriptive and statistical analysis. At first, you find out what happened, then dig into the process and causes of the development under scrutiny.  
• Reporting. All stakeholders receive the results of the analysis and plan their actions correspondingly. 

Once you implement business intelligence, you will be able to leverage all these summands to the maximum and drive much value for your business.

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What benefits does successful BI implementation bring in its wake?

Real-time data tracking

In the volatile modern world, if you wait for reports for a week or two, the information they contain may be irrelevant when it reaches the decision-makers. Your reaction may be inadequate and overly delayed. You may also fail to notice an ominous development and prevent its adverse consequences. BI implementation provides instant access to all critical data and allows you to be proactive rather than reactive in your business choices. 

Data harmonization

Very often, fragmented data different departments possess prevents them from correctly observing the entire picture. By implementing business intelligence, you will get a single version of the truth, which is crucial for data-driven decision-making. 

Enhanced visibility

Perusing hundreds of pages with statistics is tiresome and bewildering. Successful business intelligence implementation enables you to get a clear vision of all that happens in your organization (workflow-wise) and highlight areas and processes that you wish to focus your attention on.  

Efficiency of your organization

Realizing problem areas is only the first stage. You should make conclusions about where your employees underperform, eliminate redundant roles, and take other steps to optimize or even drastically improve your operations. Moreover, you will be able to see the implications of your actions, realize whether they have the desired effect, and fine-tune or even revamp your strategy accordingly. 

Deeper customer insights

Better understanding your clients’ behavior and purchasing motifs that form certain patterns is key to improving your products and customer service.  

Consistent sales insights

Having real-time as well as historic sales and marketing data at your disposal, you will be able to spot persistent trends in the niche, predict the success of certain sectors, and thus detect ways to increase revenues riding the tide.  

A competitive edge

Business intelligence can be directed not only at the processes inside your organization but outside as well. You can compare your metrics with the competitors’ indices and see where you lag behind and what they are doing better. Armed with this awareness, you can start planning a campaign to win their customers to your side. 

We hope that now you are convinced that business intelligence is a coal-and-ice practice for a modern enterprise of any size. How should you go about it? 

Before you learn how to approach and work with BI directly, you should first discover the essential types of business intelligence that a business can use to source information. Here are some heads-up and pro tips.  

Operational BI 

The first and most basic type of BI comes from the business’s everyday operations and workflow activities (company, startup, project, you name it). All the sales reports accumulated, production processes tracked, customer service and internal management decisions, and shifts made pass for operational BI. To track and gather operational BI, specialists use real-time data/activity tracking dashboards, notification systems, and regular business event monitoring.  

Strategic BI 

All the business intelligence related to internal business operations, corporate maintenance and development plans, marketing strategizing, and long-term planning is deemed strategic. Strategic BI may also be sourced from the results of market trends analysis, competitive research, and financial reports. Specialized tools for strategic planning and analytics, custom scorecards, and analysis summaries help extract this type of BI.   

Location Intelligence 

Geographic data that you gather, share, and process during business operations can actually tell a lot about about your service availability and target audience demographics, among other things. Location BI can help personalize and localize the business.  

Mapping and geo-analysis tools, Geographic Information Systems (GIS), and other solutions can help efficiently extract geo data (which can be used to optimize and improve logistics and delivery, enable product selection based on availability by region, and much more). 

Big Data BI 

We should also separately note the huge datasets generated by Big Data-enabled systems. Among all the voluminous, disparate data you gather from a bunch of connected sources, there is a ton of precious BI. Using distributed computing techniques, NoSQL databases, and platforms like Spark and Hadoop, you can structure and automate the way you collect complex Big Data. 

Real-time BI 

Tasks like fraud detection and prevention, social media or website monitoring, or targeted stock market analysis require downtime-free operation for truly efficient results. Data can be gathered in real time with the help of in-memory processing techniques and streaming analytics, giving you immediate, very dynamic insights.  

Analytics-ready BI 

Various methods of data analytics available to us today spawn different underlying types of BI that are extracted as a result of specific approaches to the analytics itself: 

• Descriptive BI includes reports, metrics, and statements analyzed as part of descriptive data analytics meant to summarize historical data and see the big picture of everything.  
• Diagnostic BI includes investigations into business shifts and changes, customer feedback analysis data, performance-affecting factors, and other takeaways that may explain the root causes of certain issues and tendencies. 
• Predictive BI includes forecasts, demand planning data, and predictions (e.g., related to conversion rates or customer churn) extracted through machine learning models, forecasting, and statistical modeling to predict future business outcomes. 
• Prescriptive BI includes the data extracted from the supply chain, inventory systems, strategic marketing summaries, and other sources that can help direct business decisions and actions with predictive insights.  

Business intelligence implementation challenges

With numerous benefits and opportunities, BI poses certain challenges you must be ready to overcome in order to really leverage all the data. Here are some of the most common ones and our recommendations on how you can deal with each.  

Data integration 

First and foremost, you don’t want to build a huge, unorganized data silo out of all the data flowing in from disparate sources, in unstructured shapes and formats. Overall poor quality of the extracted data is another underlying pitfall.  

Thankfully, all you need to handle the chaotic data collection is some well-tried data integration middleware and BI software tools. Adopting data governance practices (like multi-level access) can help further maintain the quality of collected input. 

Resource allocation and costs 

A good approach to BI integration and analysis requires major upfront costs and multi-faceted initial investments. You will need to purchase software, possibly hardware, and hire specialized experts. In addition, count on the necessities like ongoing maintenance, system improvements, and personnel education.  

To manage costs carefully, lead your BI system’s implementation or integration phase by phase, analyzing the RoI in detail before and during every stage of the process. You can also cut down on some infrastructure expenses by choosing cloud BI solutions. 

User adoption 

Not everybody is up and ready to start using new advanced tools. With BI analysis and management, skills like expert data input and processing, documentation management, and others can be required.  

This tech adoption gap can only be tackled through early and gradual introduction of the new tools, with custom guides and instructions, workshops and training courses, preferably live mentors from the staff.  

Complexity and scalability 

Business Intelligence systems are usually technically complex and can easily become a bottleneck when it comes time to scale and you don’t know how (or don’t have the budget for that).  

The only recommendation here is to pick scalable BI solutions from the start, employ good tech leads, or consult with Senior professionals before scaling.  

Security and privacy 

Of course, there are tons of sensitive corporate and user data that you must provide access to BI systems and manipulate via BI tools. With all the interactions involved, the data must still be reliably protected from unauthorized access, breach attempts, and regulatory non-compliance. 

Multi-layered data access controls, data encryption, resilient cybersecurity software, and security system audits are some of the measures to help you reinforce the security and privacy of all valuable data.  

Trends in business intelligence implementation

For the last bit of overhead information, let’s also take a look at the most demanded use cases and trends in BI implementation.  

1. AI+ML integrations. The main driving trend in the niche of BI is a smart approach to data collection and management through Artificial Intelligence and Machine Learning. Combining these smart technologies, businesses can achieve solutions with a more in-depth reach into unstructured data and predictive insights for forecasting and planning (let alone all the smart automation you get).  
2. Self-servicing BI. Specialized self-service BI tools can be introduced to help non-technical users handle complicated data management responsibilities, like report generation or dashboard creation based on statistical data.  
3. Data visualization. High-level visualization of the collected data makes professional communication and business management that much easier. Especially when storytelling elements (like narrated, chronologically ordered slides) are thrown in.  
4. Real-time analytics. The dynamic market calls for a fast take on all business aspects if that business is looking to maintain a competitive edge. That is why you should most definitely focus on real-time analytical capacities, which can be implemented through advanced and custom software tools.  
5. Cloud BI. Cloud-powered data tools have long become recognized and widely adopted for their flexibility and availability in line with specific business and tech needs. Cloud helps save lots of hidden and unnecessary expenses by charging you on-demand, for the exact amount of the cloud space used (read: rented, if you will).  
6. Collaborative BI. Teamwork is still the best work — you can achieve the next level of BI handling efficiency through collaboration. Make sure to equip your BI-enabled platform with collaborative features for data editing and analysis.  

Business intelligence implementation steps: An algorithm to follow

Implementing business intelligence isn’t hard, but it takes meticulous preparation and rigorous following the agreed-upon plan. 

Step 1. Develop a business intelligence implementation strategy 

With dozens of business projects of different kinds, we at DICEUS know well that any complex and multi-phased endeavor will triumph only when the team thoroughly plans it. Without a well-defined strategy, employees refer to discrepant data, follow conflicting instructions concocted on the fly, and take guesses at unclear names and definitions. What makes matters worse, during such randomized and haphazard efforts, data quality becomes only an afterthought.  

While developing the roadmap, you should answer three basic questions: What do we have? What is our goal? What do we need? 

First of all, make a roster of available data and departments that are responsible for storing it. Then, try to understand what other data and systems you will need. 

Secondly, shape your objectives. You should clearly realize where you would use BI insights. Do you want to improve your workflow? Or reduce customer churn? Do you want to optimize your expenditures? By setting priorities, you will be able to choose efficient ways of reaching your goals. 

Thirdly, form a vision of your future BI system. Should it integrate the existing disparate structures, or should it totally replace them? What functionalities would you like it to have? Should it be an on-premise network, or would hydrating the cloud suit you better? 

Only after you have come to grips with all minute details of the future undertaking can you go at it hammer and tongs. 

Step 2. Appoint the team responsible for BI implementation 

Once the plan is drawn, you must decide who will see it through. Since business intelligence is not just an IT or financial project but essentially an across-the-board issue, the implementation team should include representatives of all departments. Moreover, all stakeholders who work with business data should participate in the venture. To mitigate conventional resistance to change, the entire company’s personnel should be carefully explained how each will benefit from BI implementation. 

Step 3. Define KPIs to be subject to BI 

State-of-the-art software tools can yield dozens, if not hundreds, of various indices. Don’t try to boil the ocean and cull tons of them. Go through your company’s pain points and business goals to check which KPIs are critical, which are just useful perks, and which you can skip.  

To streamline your selection throes, split KPIs into categories: project management metrics (ROI and productivity), marketing data (conversion rate, customer acquisition cost, cost per lead), financial indicators (net income, liquidity ratio, sales growth), customer metrics (customer lifetime value, monthly number of new customers, social media traffic), HR indices (cost per hire, net income per employee), etc. 

It is also necessary to know which of the KPIs you would gauge across the organization and which ones within the departments. 

Step 4. Find a competent software vendor 

Contemporary business is heavily IT-reliant, and implementing business intelligence requires utilizing a set of corresponding software tools. Of course, you can try to do it on your own and pin your hopes on the in-house team. In this case, it should consist of a BI infrastructure architect, a data and a database administrator, a data mining specialist, an application lead and an ETL lead developer, a data quality analyst, and a project manager. Do you have all of them on your payroll?   

Evidently, large-scale projects that necessitate specific expertise seldom (if not never) benefit from a DIY approach. That is why outsourcing BI implementation is a wise solution.  

Searching for a vendor, you must make sure the company of your choice has sufficient experience in the domain, a qualified team of specialists, and the knowledge of the required tech stack, and offers high-end services at a reasonable price. With DICEUS, you can tick all the boxes. We can handlany complexity of e a BI implementation projecy and deliver an excellent outcome within a stipulated time and budget. 

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Step 5. Select the right BI implementation tools 

Today, there is a wide range of business intelligence tools on the market, so to navigate the plethora of options well, you should consider both your functional needs and non-functional requirements (security, performance, availability, etc.). The way you will access the data, visualize it, and interact with the metrics greatly affects your choice. 

Out-of-the-box solutions will suffice if your data intelligence efforts are medium-scope. But if you plan to dig deep into analytics, consider acquiring customized tools that can be tailored to perfectly match your vision of business intelligence. 

However, the best course of action is to entrust all technical details to the recruited vendor, which can avoid the headache of choosing BI tools.  

Step 6. Take thought for infrastructure 

The two crucial infrastructure elements related to implementing business intelligence projects are data storage and the BI platform. 

Today, on-premise data banks are considered to be obsolete and unwieldy behemoths that can hardly keep abreast of ever-growing volumes of data and shifting demands imposed on data handling. That is why most organizations opt for enterprise data warehouses (EDW) that can hoard larger amounts of data from multiple systems and apps (ERP, CRM, HRM, etc.) and provide efficient processing of it.  

Equally outdated are local BI platforms, which are increasingly being ousted by cloud facilities. To balance the level of control and storage cost offered by either option, you can try hybrid solutions that combine the best of the two worlds. 

Step 7. Prepare the data 

By all accounts, this stage takes up to four-fifths of all the time BI development requires. First of all, data should be synchronized since various departments may use different tools, approaches, codes, storage patterns, etc. – the phenomenon known as data silos. When the integrity of data is achieved, there are other parameters related to data quality you should look into (completeness, validity, consistency, relevance, accuracy, uniqueness). 

Step 8. Perform data migration (optional) 

In case you realize that the on-premise data storage and BI platform that you use hamper effective business intelligence, consider hydrating the cloud. DICEUS is ready to consult you on the matter and provide high-profile data migration services.

Step 9. Initiate a feedback loop 

You should schedule regular meetings with BI stakeholders to review the current progress of the project and introduce timely corrections. You can launch a trial with a few vital KPIs. Collect feedback on its success and the detected bottlenecks to remedy the overall strategy (if necessary). 

Step 10. BI implementation on a larger scale 

Once you make sure you have introduced requisite adjustments and your strategy works smoothly, proceed to implement business intelligence with other KPIs. But remember to assess the procedure ever and again and optimize the problem areas to see the whole project through and get at the expected deliverables.  

Business intelligence is an imperative practice that supplies enterprises with critical information on their operation. It enables them to evaluate performance, expose deficiencies, and map out strategies to improve workflow efficiency and customer engagement. By employing the services of a seasoned outsourcer, you will be able to drive positive change to your company and significantly facilitate its business activities.

Benefits of BI implementation with DICEUS

The above article should give you a clear idea of how to implement business intelligence solutions in the most educated, safe way. At DICEUS, we can help you turn all this theory into action and handle the full range of your BI planning, implementation, and adoption needs.  

DICEUS is a team of seasoned specialists with high qualifications and relevant experience ready to deliver BI projects that are: 

• Built on proven expertise — we are a one-stop IT and software services provider to handle the full lifecycle of your BI project and help find the best-fitting ways to adopt and leverage complex technology. 
• Custom and personalized for the TA — we tailor our creations to unique needs and help customize BI solutions for individual user audiences with different tech skill levels. 
• Simple to integrate — we find ways to easily bridge the gap and connect new BI tools with existing platforms and systems, even legacy ones. 
• Focused on boosted data quality — DICEUS leverages ETL (Extract, Transform, Load) techniques and tailored data governance frameworks to boost the quality of BI as much as possible.  
• Resilient, flexible, and scalable — we put a slew of expertise into your BI project, providing easy-to-use, reliable custom tools that can be easily scaled to handle a growing flow of incoming data. 

We also support and consult BI projects at every step, helping business owners, managers, and stakeholders achieve the desired results without spending a fortune.  

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