Business Intelligence Tools Roadmap – Business intelligence is responsible for why some companies leave others behind. We provide data-centric solutions that enable our clients to achieve great success by improving their decision-making ability.
Since the beginning of mankind, people have generated data, be it stone tablets or markings in caves. The real boom came after the industrial revolution, when people started developing processes and benefiting from automation. This was the age of processing and storage. Therefore, the processing power and storage capacities of computers allowed us to write complex workflows and automate processes. As a result, enterprise resource planning (ERP) software was developed by SAP, Microsoft, IBM and Oracle. Similarly, Customer Relationship Management (CRM) programs followed and data collection began. Internal data generated by operational application, marketing and customer data, along with data from social media and other external sources that complement the business. As a result, both the volume and variety of business data increased.
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In the early days, Management Information (MIS) and Executive Information Support (EIS) systems generated data for executive decision-making on an as-needed basis. This practice led to reactive decision-making. To proactively lead executives, data warehouse and BI platforms have been developed to provide decision makers with live data, visualizations and dashboards. That’s what we do!
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After all, business intelligence consists of technologies and technical processes that enable companies to use this data. These include artificial intelligence and machine learning algorithms that allow companies to predict customer behavior.
At DM, we use our proven approach of first understanding business goals and objectives for a BI implementation. Followed by considerations of existing data sources and the prevailing IT environment. Finally, the type and variety of applications running in the organization. Taking into account the information gathered, we develop a BI implementation roadmap that highlights the best technology stack / BI platform for business. This leads to interaction with business units and KPI cataloging and selection of the best approach to design DW. Consequently, business and reporting requirements are collected and mock dashboards are developed and approved. A data health report is developed to help data engineers clean the data. ETL is developed considering the requirements and observations in the data health report. Selected data tables or variables contained therein are extracted, transformed and loaded into the DW.
Depending on the BI platform selected, data modeling is completed leading to the development of a presentation layer, reports and self-service/ad hoc analysis capabilities on the OLAP cube. Advanced analysis techniques such as what-if analysis, predictive and prescriptive analysis capabilities are developed using either the built-in capabilities of the BI platform or other tools/APIs.
At DM we have an excellent command of both BI platforms developed for companies and visualization tools. With 30+ completed BI projects and 20+ certified resources, DM is one of the leading end-to-end business intelligence and data management companies. Below are the platforms we have used in the past.
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In connection with the development of a data warehouse and a presentation layer, we use components from enterprise platforms to develop ETL, data modeling and advanced analytics. Certain scenarios require tools with standalone data management capabilities. Here is a list of tools we have experience with;
After an implementation of a traditional business intelligence project or even independently, our consultants use sophisticated prediction and forecasting techniques. These techniques include data mining, neural networks, pattern recognition, sentiment and sentiment analysis, and cluster analysis. We go beyond simple math and use statistics and algorithms to uncover deep insights for your business. We achieve all of this while remaining cost-effective by opting for open-source tools or leveraging the capabilities of your enterprise business intelligence platform. Alternatively, DM performs a case-based data analysis service that includes assessment, evaluation, delivery, cleansing and final analysis of datasets and reporting.
Our BDA projects consist of these main phases, aligned with the platforms and tools we use to accomplish these tasks.
At Data Mustang, we live to create technology solutions; to harness the power of data and uncover meaningful insights to improve decision-making. Business Intelligence is the means to turn raw data into useful insights. It allows you to collect data from different sources, organize it and then enjoy the analytics. Do you need it? Probably yes, as it’s the most balanced view of the business you can get. But as you can imagine, starting such a complex endeavor requires some preparation, and we hope to help you with that. And if you already have some BI practices in place in your organization, this article will help you get organized as well. Now let’s talk about your business intelligence strategy.
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A BI strategy enables you to address all your data problems and needs, develop a coherent system and maintain it. What happens when you start implementing BI without a strategy? Basically, you are too focused on getting those graphics, but nobody in the company understands why and how they are used. Here’s a practical illustration of life before and after a BI strategy.
Previously, we published a guide to implementing BI practice in your organization. Now let’s detail one of the phases of your implementation plan – documenting a BI strategy.
Vision. Why are you building the BI practice in your company and what do you want to achieve?
Tools and architecture. Which dashboards and solutions do we want to build? For which areas? And how will they affect these areas?
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So when you look at your company’s business strategy, imagine what BI initiatives you want to launch. This vision then helps you select the right people to use and maintain the selected processes. To support people and processes, you use software tools. And finally, you establish the architectural design for its development. Now let’s go into these steps in detail. And we’ll start with the companies that already have some form of BI.
To know where you’re going, you need to set a baseline. Assuming you know that multiple departments have been using analytics, but the data has been largely siled – marketing reps don’t have access to sales information and customer support is tracking user feedback for their own internal purposes, or there may not be any analytics at all – basically it seems to work, but how effective is unclear.
So start by talking to all those involved in the current BI processes: users and the IT team, department heads and stakeholders. As a result, you should have answers to the following questions:
Then put together a SWOT analysis to organize what you found. One of the most important strategy development tools, SWOT analysis helps you uncover your key assets and pain points for the next phase.
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A vision is a combination of purpose and direction. No strategy without a vision. It manifests itself in the form of many crucial decisions, e.g. B. what data we collect or who gets access to the findings.
A vision also has a very mundane purpose: to explain to people in your organization – who already have their favorite tools and processes – why they need new ones and how the transformation will happen.
Our corporate vision for BI is to create and support an infrastructure with secure and authorized access to data residing throughout the enterprise. Our corporate standard for a BI tool is ________. We staff and measure our BI Center of Excellence based on end-user satisfaction surveys and successful implementations. An important segment of our end-user community requires near real-time data access. So we have provided such infrastructure to accommodate them. We currently support ___ users, representing ___% of our user population. Our goal is to increase usage by ___% by (date). We weigh the potential costs of increased BI adoption against the business value and ROI we receive. This gives us a clear view of our success that is measurable, accountable and defensible.
Biere also notes that having a vision so articulated will help ward off the executives whose only goal is to arm employees with cheap tools and get back to “real work.”
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Finally, the vision is often accompanied by the BI Maturity Model – the scale that tells you how mature your strategy is. You can find some interpretations of maturity models on the internet, a very popular model comes from Gartner. Accordingly, there are five levels of maturity for BI: Unaware, Opportunistic, Standards, Enterprise, Transformative. These models can help you identify your development areas and your next goals.
BI governance is about defining and implementing the BI infrastructure. (Not to be confused with data governance, which ensures the consistent use of data across an organization.) This involves three components:
Define BI governance members, their roles and responsibilities, functions, goals and relationships to various structures in your organization. Involve people at all levels, from executives to end users, to bring all their perspectives. So it’s not really a group of BI experts, but a committee made up of representatives from different areas of the company.
However, larger companies would benefit
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