Successful implementation of Business Intelligence: An ultimate guide
N-iX
2020-07-31T20:25:25+00:00

Companies in the top three spots in their industry that rely on data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors [3]. Modern companies collect tons of data. From information about their expenses to statistics about their processes and ...

Successful implementation of Business Intelligence: An ultimate guide
Companies in the top three spots in their industry that rely on data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors [3].

Modern companies collect tons of data. From information about their expenses to statistics about their processes and clients. However, many of them either do not understand the full potential of the data or don’t leverage it at all. If you are looking for a way to optimize your operations, understand what is happening, and even predict certain changes in the way your business, you will need to implement Business Intelligence. In this article, we are going to take a look:

Business intelligence (BI) refers to strategies, software, and technology for data analysis of business information. Successful BI implementation allows you to transform data at hand into actionable insights for both strategic and tactical business decisions. Typically, BI relies on large amounts of structured data (although there are cases when unstructured data can be used) to create the ground for informed and effective decisions. 

Importance of Business Intelligence

It’s rather hard to overrate the value of a properly implemented BI to any company. Not only does it help improve access to data and its use for business insights, but it can also help increase profitability, gain competitive advantage, and accelerate overall growth. Among other benefits of business intelligence implementation are:

  • Improved decision-making process, based on the informed choice;
  • Optimized business processes, both internal and client-facing ones;
  • Increased operational efficiency, as ineffective business processes can be easily identified;
  • And a single version of the truth that will help gather the fragmented versions of data into a single bigger picture.

So, the next question is, how can you successfully implement Business Intelligence in your company? Let’s take a look at the steps that will lead you to a successful business intelligence implementation.

10 steps for Business Intelligence strategy implementation

A well-planned business intelligence implementation can become a highly beneficial tool for C-level executives and their departments in understanding their processes and results. It can also help you understand your customers more. We are going to focus more on the outsourcing of BI implementation, however, these steps are applicable if you choose to hire an in-house team as well. 

  1. Create a business intelligence strategy 

A business intelligence strategy is a blueprint that allows any company to measure its performance, expose shortcomings, improve competitive advantages, and use data mining and analytics for successful decision making. Any implementation is impossible without a clear understanding of the key elements:

  • What’s your objective?
  • What do you have?
  • What do you need?

critical success factors for implementing business intelligence: objective, resource, plan

Once you can answer these questions, you can start working on your strategy or roadmap. Depending on the maturity level of the company, previous experience in BI adoption or lack of thereof, and size of the company, the final results may vary. 

Business intelligence implementation: stages of becoming a data mature company

If you have no idea where to begin and choose to outsource, make sure to discuss Discovery Phase with your vendor. They will help you identify your needs, and a potential solution that matches your needs, as well as plan the execution.

  1. Set the Key Performance Indicators

Once you have gathered enough information, it is important to define the KPIs you are going to track on the company-wide scale, and KPIs to track within the departments. Your KPIs should be measurable, matching your objectives, and vital for achieving your business goals.

Example of KPIs in business intelligence implementation

  1. Appoint stakeholders and educate the staff

One of the first business intelligence implementation challenges is a human tendency to resist change. The most effective way to minimize resistance is to educate your personnel. If your company has no previous experience with BI, you must explain how each department can benefit from BI implementation. You should also determine key stakeholders in each department. They will help you collect and prioritize pain points and key performance indicators (KPIs) across the company.

  1. Build a strong BI team or outsource

BI is a cathedral. And every team member plays a vital role in its strength, majesty and beauty. Or can contribute to its failure. A BI team that has a clear vision and remains focused on that vision throughout their tasks will work with synergy, enthusiasm and creativity that can never be nurtured in a scattered group of individuals, regardless of their talent and experience. ~ Douglas McDowell, CEO of SolidQ North America

In our guide Business intelligence outsourcing: how to make it work?, we have discussed what a good business intelligence developer should know and where to find a BI team. If you are outsourcing business intelligence implementation, your vendor will offer the team structure that best suits your needs. However, if you choose to create an in-house team, here’s an example of a team structure.

BI team structure: business intelligence developer, architect, lead, analyst, data mining expert

The technical stack behind business intelligence solutions can be versatile and depends on the chosen software and your business needs. Some of the most common technologies used are MS SQL, Oracle, MySQL Hbase, BigSQL Data Lakes, AWS Redshift, Apache Spark & Hadoop, SSIS, SSAS, Pentaho, Tableau, QlikView, Power BI, and others.

BI implementation tech stack

  1. Find the best software for your needs

The selection of tools will vary upon the requirement and budget. However, it is crucial to understand and evaluate these factors while choosing a software solution: 

  • Do you have access to data and a convenient view of the relevant information?
  • Does this system offer integration within the existing systems or APIs to connect to your systems?
  • How can you interact with data within a visual interface of the software?
  • Can you collaborate with others on data analysis and share visualized analytics?
  • Will you be able to dive deeper into data and discover new insights on your own? 

Best business intelligence software

How to face software challenges in business intelligence implementation? 

  • As tempting as it may seem, settling for an all-in-one BI suite might not be the best idea. Unless you are 100% sure, you might need to find alternative software for each task or part of BI.
  • Either discuss a long-term evaluation license or deliberate several less inexpensive BI components from different providers.
  • Whenever possible, first use licenses and tools already available within the organization. Purchasing additional licensing might be cheaper than working with a completely different software provider. 
  • Accept that different user groups or departments might need different user-facing tools.
  • Keep in mind that any solution should also match non-functional requirements like availability, security, and performance.
  1. Choose your data storage, environment, and platform

If you don’t have the infrastructure, it’s a good idea to start with choosing your data storage option. Typically, data warehouse is considered a more suitable choice for Business Intelligence implementation, as it provides an analysis of relational data coming from both online transaction processing (OLTP) systems and business apps (e.g., ERP, CRM, and HRM systems). However, many companies practice adoption of both types of data storages, reaching the maximum potential of their BI systems.

BI implementation: choose the right data storage type

Keep reading: Data lake vs data warehouse: What is your best choice?

After selecting the software that matches your business needs and works well with existing infrastructure, it’s the time to find the right environment and the BI platform. You can decide whether you want an on-premise, cloud-based , or hybrid environment.Choose the right environment for business intelligence implementation

If you choose a Cloud environment, you will need to choose a platform that will host your Business Intelligence.  Keep in mind that the environment and platform choice will affect your application's architecture, so it's better to have an architect present during the discussion. 

Top BI platforms in the world

Why does the choice of data storage, environment, and platform matter so much? Let’s take data storage as an example. Data lake uses the ELT (Extract Load Transform) procedure -  the data is processed after it is loaded into a data lake. The data warehouse uses the ETL (Extract Transform Load) procedure - the data is transformed and then loaded into the data storage. So this choice will define when the data is processed and what type of data can be analyzed. 

  1. Finetune your data preparation process

Poor data quality costs the US economy around $3.1 trillion annually [1].

Often, big organizations struggle with large amounts of useless data, a.k.a 'data silos'. It happens when teams or departments use different tools, have entirely different approaches, and keep data to themselves. Typically, data preparation takes up to 80% of BI development time. 

Data quality characteristics

Any successful business intelligence implementation relies heavily on high-quality data. According to the study, more than 63% of respondents say that data preparation for business intelligence implementation is either ‘very important’ or ‘critical’.

Importance of data preparation for successful Business intelligence strategy implementation

  1. Consider more advanced solutions 

As of 2019, 91.6% of global companies said they are increasing their investments in big data and AI [2]. If you rely heavily on Business Intelligence, its implementation might be even more beneficial if you incorporate more advanced technologies like Machine Learning. It can also help you:

  • Build optimized data pipelines;
  • Achieve real-time data analysis;
  • Make actual forecasts;
  • And analyze larger sets of data.
  1. Implement the PoC or a pilot project

Once you have all the processes ready, it is high time for a test run. And while it might seem like a great idea to test the system on the company-wide scale, it is better to have a pilot project within a smaller group.PoC or pilot project implementation of business intelligence project

  1. Implement the changes to meet the KPIs

And we are back to steps 1 and 2. Review your results and see whether you've met the initial expectations. If not, see what can be done to achieve the initial KPIs. Once you implemented the changes, run another pilot to understand how much you have covered between these two pilot runs and how this changes the picture. It is a continual process, and it needs optimization at every run until all of the involved parties are happy with the result. Once you reach that point, you can safely scale up. 

Types of deliverables you can get from Business Intelligence implementation

Business intelligence combines a broad set of data analysis applications. Depending on your needs, available data, tech stack, and the type of the task at hand here are the most common deliverables of Business Intelligence implementation:

  • Ad hoc analytics helps you answer a single business question. Focusing on a specific issue, this tool can either generate a report that does not already exist or dig deeper into a static report to get additional details about a particular business process or part of operations.
  • Online analytical processing (OLAP) allows users to extract and query certain data in order to analyze it from different points of view. It is typically used to analyze trends, financial reporting, sales forecasting, or other planning purposes.
  • Real-time BI. Real-time business intelligence enables users to get up-to-the-minute data by accessing operational systems or feeding business information into a real-time data warehouse and/or BI system. 
  • Operational BI. Operational intelligence is an approach to data analysis that enables business operations decisions and actions to be based on real-time data as it's generated or collected by companies. Typically, the data analysis process is automated, and the resulting information is integrated into operational systems for immediate use by business managers and workers.
  • Collaborative BI emerged through combining business intelligence software with collaboration tools to support improved data-driven decision making.
  • BI dashboards and data visualization display key business metrics at a glance.

Business Intelligence development: How to build and use BI solutions

Why choose N-iX to help you with Business Intelligence implementation? 

N-iX is an Eastern European IT service provider with a wide range of tech expertise, including Business Intelligence implementation, Big Data analytics, data science, Machine Learning & AI, Cloud solutions, and much more. Our teams are building solutions for businesses in fintech, retail, telecom, media, automotive, healthcare, and other industries. We have helped numerous companies levelup their Business Intelligence implementation:

  • As a part of our partnership with Gogo, a global provider of in-flight broadband Internet, we migrated existing data solutions to the AWS cloud and shut down the costly on-premises infrastructure. Our team built a unified AWS-based data platform that collects and aggregates data from 20 different sources and can process up to 3 TB of streaming data per day. Adopting Data Science and Machine Learning algorithms, we developed models for both predicting satellite antennas failures and monitoring of equipment health. This allows the client to receive business insights as actionable BI reports.
  • Another strategic partnership is Lebara, Europe's fastest-growing mobile virtual network operator (MVNO). One of the streams our team has contributed to was Business Intelligence development, including performance optimization, support, and development of the existing enterprise BI solution previously supported by IBM. Our team has optimized the workflow, removed delays in reporting, and helped to reduce the supporting team by 50%.
  • From PoC to production of scalable big data analytics platform for Fortune 500 industrial supply company (under NDA). Our experts used a cloud-agnostic approach to ensure that the platform is compatible with multiple cloud vendors in case the client needs to change the cloud provider. After over six months of working on the data pipeline unification, we managed to integrate more than 100 different data sources into a unified data platform. This includes daily data loads, along with a backfill of historical data. As a result, our client can reduce the costs (for both infrastructure and overhead), leverage the unified data platform, and benefit from predictive analytics integrated into the platform. 
  • Scalable and customizable Big Data analytics system powered by IoT and ML for a leading telecom company (under NDA). We are currently working on an IoT Data Processing Platform that will collect data from multiple IoT sources (i.e. devices, sensors, GPS). The platform will allow both internal users and third parties to build customized data pipelines, export data in a convenient format, and prepare actionable predictions and reports. As a result of our cooperation with the client, we created a scalable, customizable data analytics system that can process hundreds of thousands of transactions close to real-time.
  • Another client is Cleverbridge, a company that provides full-fledged e-commerce and subscription management solutions. In order to build lucid and informative Business Intelligence reports for the company’s clients, we applied our extensive knowledge in Power BI. Not only is the client satisfied with it, but it also helped them to gain an opportunity to enhance its value proposition to other clients.
  • Orbus is a leading global provider of software solutions for Enterprise Architecture, Business Process Analysis, and Application Portfolio Management. N-iX team has been working on delivering BI development services for the Strategic Portfolio Management dashboards used by Orbus customers. This tool provides visually rich, easy-to-navigate, interactive dashboards to help Orbus customers analyze and optimize different portfolios and projects to meet their strategic objectives and increase operational productivity. BI developers at N-iX have designed and built over 100 dashboards with Microsoft Power BI, Tableau, and Qlik. 

If you have any questions about Business Intelligence implementation or BI strategy, contact our experts

References:

  1. Extracting business value from the 4 V's of big data by IBM
  2. Big Data and AI Executive Survey 2019
  3. Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?

     

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About N-iX

N-iX is an Eastern European provider of software development services with 1000+ expert software engineers onboard that power innovative technology businesses. Since 2002 we have formed strategic partnerships with a variety of global industry leaders including OpenText, Novell, Lebara, Currencycloud and over 50 other medium and large-scale businesses. With delivery centers in Ukraine, Poland, Bulgaria, and Belarus, we deliver excellence in software engineering and deep expertise in a range of verticals including finance, healthcare, hospitality, telecom, energy and enterprise content management helping our clients to innovate and implement technology transformations.

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