BI for Start Ups, Business Intelligence, Data Nerds, Learning New Things

3 first steps into becoming a Business Intelligence resource

BI - Books to start with
BI recommended beginners reading

Note that I did not write Data Scientist or Business Intelligence Expert. This is because this post is meant for those who plan to join the field with no former experience.

To become a Data Scientist or Business Intelligence Expert, you have to learn the basics of handling data first.

Here comes a starters list:

  1. You need to understand the basic business metrics and KPIsWhat data is currently used for.

Check basic KPIs for different branches and learn to the intended purpose of the measurements. Most data collected today is used for Descriptive analysis, i.e. looking back at what has happened. To start the journey towards BI and data expertise, you need to understand the basics of BI today. Some suggested books are in the above image – BI recommended beginners reading.

2. Next step is to understand how the data you are collecting today will be used in the future. This will give you invaluable insight into what data you need and how the solutions need to be designed to deal with future analysis needs.

Descriptive vs Predictive analysisIn any career, you need to be strategic. All businesses want to utilize their data for strategic actions and competitive advantage. A big part of this understanding how customers will or may act in the future. Based on customer behaviour today, what will they need in, say – one year, two years etc.

Descriptive vs Predictive analysis 23. Learn the basic techniques and methodologies of handling, processing and analyzing data.

Most of these are well described in Kimball’s Data Warehouse tool kit which has been invaluable for me. I still re-visit it, when a difficult problem arises and I have forgotten exactly how the syntax or logic should be. SQL and data modelling techniques are globally useful and a good base to build you data expertise on. Please refer to the above image – BI recommended beginners reading.  Additionally, there are numerous online resources so you are spoiled for choice! Just find something that works for you in the learning process and keep learning.

Feel free to make contact with questions or input! And leave comments with your own suggestions below.

Thanks! /Linda

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Business Intelligence, Data Nerds, Data Science, IT Management, Learning New Things

To Enjoy Data and BI, Learn to Learn and Don’t be afraid of Change

“The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” — Alvin Toffler

BI and Data Science are the artistic, creative side of finding truths in unexpected nooks and crannies of organizations and societies. Finding links and interpreting the messages being received from the different interconnections and integrations between systems to ensure a holistic view of the world and of organizations. If you want to enjoy working with data, learn to learn and unlearn everything all the time. Because what you know today, what data tells you this minute, may change in two hours.

AI and LearningAI is changing our answer to “what will you be when you grow up?” The answer is not always nurse, pilot, accountant or all the cute things we said when we were children! In this age, you can say AI manager, Robot Designer or Machine Learner without blinking an eye. It is our future and imagine it! We need to imagine it positively in order to embrace our future so we can manage it.

No fear, even the AI-predictions may change, and the course of our lives will be altered. That is OK. We will learn whatever comes.

Don’t waste energy on trying to control the change, you cannot control the change, because it is out of our small hands. Change may happen because data and statistics can be manipulated or due to the fact that our virtual, online realities are changing every hour. A post on twitter may change the sales predictions your sales team made yesterday. An online AD today may change your expansion vision for 2018.

 

 

Business Intelligence, Data Nerds, Data Science, IT Management

Business Intelligence for beginners

Are you a beginner and want to know the difference between Business Intelligence and data Science?

I have a colleague who worked in Business Intelligence in the 90s. They tell me that they worked in the IT department and their role was Business Intelligence. BI was a role. A one man show. Nowadays, Business Intelligence is a department, a function, an organization. With teams of experts in different roles.

If you are starting out, below are a few tips for a solid start:

  1. Learning the 20% of the skills that you will need 80% of the time. You do this by building a foundation in data analysis. Read some books about data and analytics. Wheeler’s Making Sense of Data is a good start. Listen to Pods about data and analytics for example @ Ted’s.
  2. Learn a code language for Data Handling. ETL and SQL are a good starting point. If you are feeling  challenged and motivated, get up to speed on the basics of R programming or visit DataCamp for free.Or some BI language/tool of your choice.
  3. Lastly, learn the different tasks that need to be performed in a well functioning Business Intelligence/Analytics department. Infrastructure, Architecture, Best Practices, Innovation, Communication etc. If you want to work with data, you will need to learn how to work with other data handlers because data is a traveler. It stops at different stations, picks up new attributes, and then continues on its journey towards you. You will need to understand the road traveled by the data that is coming towards you and the hands that are handling this data for you to be able to deliver sane, reliable data.

Don’t think math. Although you will need some math, you will do very well with minimal math. What you need to understand is logical business processes and their resulting data points.

 

BI Tools, Business Intelligence, Graphic User Interface Design, IT Management

An Introduction to Business Intelligence

Gartner defines Business intelligence (BI) as:

An umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.

It is an OK definition. I like OLAP’s definition better:

The term Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information. The purpose of Business Intelligence is to support better business decision making. Essentially, Business Intelligence systems are data-driven Decision Support Systems (DSS).

Finance Online and other more revenue focused organizations go into details and lists about what constitutes Business Intelligence. For example:

  1. Data mining
  2. Online analytical processing
  3. Querying
  4. Reporting

A break down of Business Intelligence (BI) is made at Investopedia:

Business intelligence grew out of the conviction that managers with inaccurate or incomplete information will tend, on average, to make worse decisions than if they had better information. Creators of financial models will recognize this as a “garbage in, garbage out problem.” Business intelligence is meant to solve that problem by bringing in the most current data that is ideally presented in a dashboard of quick metrics designed to support better decisions.

As you can see, Business Intelligence is both simple and complex in all the ways it should be.

I would like to suggest that:

Business intelligence (BI) is growing out of the knowledge that decision makers with selective/subjective, inaccurate, reliable, manipulated or incomplete information tend, on average, to make catastrophic decisions. This, as compared to better decisions made based on accurate, reliable, non-manipulated and complete information from multiple objective sources.

In decision making, as in Business Intelligence, the keywords are “Crap in, Crap out.” Therefore, BI’s main goal should be to reduce the challenges faced in Decision Making by bringing in the most current data – accurate, reliable, non-manipulated and complete – from multiple objective sources.

To make this data available and fathomable for decision makers, it should ideally be visualized in user friendly presentation tools chosen with great consideration for the specific decision maker’s needs and technical maturity.

To explain what I mean, I will give the example of the yearly/quarterly comparison reports for BI tools. Every new report announces the latest “best” tool in different categories.

The only message missing from the comparison report is:cropped-db-image.png

Please note before you purchase this fantastic BI tool: It doesn’t matter how fantastic the GUI (Graphic User Interface) is, in BI & analytics, if the data is crap, the decision maker will not be jubilant about the GUI.

One of the lessons I have learnt working with BI is that, the frustration caused by unreliable data, overrides the pleasure of a beautiful GUI, every single time.