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

#Mindmaps for Making Sense of Data

Continual Improvement

I have had a little time in between all the data quality issues so I am practicing the mind-maps.

When i was student, I used mind-maps, use-cases and information models to make notes. I am a connection kind of person, I learn by connecting one thing to the other. I am reading through Donald J. Wheeler’s, Making sense of data. To understand it all, I will need all the mind-maps I can make.

In the 1st chapter, Mr. Wheeler presents quite briefly the Continual Improvement approach.

Advertisements
Business Intelligence, Data Nerds, Data Science, IT Management

Data Quality is Our Day Job

Data quality should be everyone’s job. And it is in the business intelligence department. It is not a glamorous dress up, smile and relax profession. It is a sweaty, bloody and teary frustrating grind. Some days, it is stimulating and fun!

50% of working hrs — BI resources are in hidden data factories. Hunting for data, finding and correcting errors, and searching for confirmatory sources for data they don’t trust.

60% of working hrs — Data scientists spend cleaning and organizing data.

75% — Total cost associated with hidden data factories in simple operations, where resources are (1) Verifying/validating data quality or (2) Resolving data quality issues.

Friday Afternoon MeasurementThere are those who have successfully tried the Friday Afternoon Measurement – Spending Friday afternoons measuring the data quality for the previous week. This also helps to qualify and quantify the activities that will be needed to improve data quality the following week.

The One in Ten Rule is also a good method/tool to measure data quality.

You cannot change that which you do not know or understand.

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.

 

 

AI and Business Intelligence, Business Intelligence, Data Science

Data And BI Trends in 2018

Data and Business Intelligence are different ends of the same stick. Or, if you wish, different sides of the same coin. Like Petroleum (Crude oil) and Fuel so to speak. Data and Petroleum being the raw materials. The expense. Business Intelligence and Fuel being the assets. The end products. The income earners.

The predictions for 2018 are as exciting as they are challenging. I would like to name 3 of the most exciting for me:

  1. Analytic is still high up on the list. Analytic go hand in hand with Machine Learning. The biggest cost within Business Intelligence is the preparation and sanitizing of data. Of course, most businesses are working tirelessly to find ways to save a coin by automating data preparation, discovery and sharing and collaboration. That is where machine learning comes in.
  2. Smart Technological Gadgets will become Smarter. New generation digital citizens will expect no less. With AI agents and AI logic, gadgets and apps will be more interactive, conversational, predictive and intuitive to events and needs. All this Smartness and intuitiveness will be dependent on Data – the collection, the handling and the utilization of data.
  3. The Cloud and The Edge will be married. The courting period has gone well. Cloud services have been moving closer to data sources and cloud models have become more service oriented which has shortened the distance between Cloud and Edge considerably. On the other hand, Edge’s centralization and coordinated structure has become data smart and service oriented.

Read more about these and more in the links below. You are Welcome! Happy Reading!

Dataversity

Gartner

Mckinsey

 

 

AI and Business Intelligence, Business Intelligence, Data Nerds, Data Science

AI, Star Wars and Droid Intelligence

c3poSo I saw Star Wars the Last Jedi, taking no regard to the User Reviews on IMDB.

My love for Star Wars has nothing to do with Princess Leia, Han Solo or Luke Skywalker, handsome though they may be. Not even the Force impresses me. It is the droids that do it for me.

First, it was C3-PO and R2-D2. To save secret information in R2, and make sure that she, yes she, had the sense (emotional intelligence) and understanding  to show it only to trusted allies. Help me Obi Wan Kanobi. Okay, fine, I liked Yoda, Chewbacca and the other extraordinary creatures of the Stars. But the droids win every single time.

star wars 2When C3-PO rambles on about the statistics in his neurotic little chip head, tries to run away from every single tight spot they get into and understands the hows and whys of being an obedient droid to new owners. Knows when he has been bought, literally. When R2 scans rooms to find ports where he can stick his little fingers and open doors that are otherwise locked, without loosing his playful presence, I swoon. Later came BB-8, who can dance with his head rolling all over him. When someone threw BB-8 down the stairs of Canto Bight, I flinched. Just as I do when I see someone treating another person badly, violently or disrespectfully.

star wars 1I work in BI – finding, collecting, transforming, structuring and making available the data that AI will be using to make those smart predictive algorithms that improve customer relations, sales and sustainability. However, I am looking forward to becoming a team lead, or #personality-designer for robots, droids, drones, Siri, Alexa who controls or works with Nest, Tesla, John Paul (no relation to John Paul the 2nd), Amazon, Netflix and all the other variants of AI. If AI-agents come with the personalities that were dreamed up, designed and pioneered by #StarWars, among other AI pioneers, I may even switch competence areas and join the AI #PersonalityDesignTeam. To be one of those who are designing and engineering AI that is interesting, diversified and fun to have a coffee with.

c3po 1New buzz words:

AI PersonalityDesign: The design and refinement of AI personality traits and characteristics with the purpose of adjusting each AI to its role, area of operation and/or branch.

 

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.