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 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 is how customers will or may act in the future. Based on customer behavior today, what will they need in, say – one year, two years etc.

Descriptive vs Predictive analysis 23. Learn the basic techniques and methodologies for 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. Among others, SQL and data modelling techniques are globally useful and a good base to build your 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

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.

Business Intelligence, Data Nerds, Learning New Things

The Business Intelligence of Money

This post is triggered by the January financial woes. Everybody, or most us at least,  Financial freedom - Money analysisagree that right about this week, January feels like the longest month of the year.

Apart from time, money is one of the most analyzed values. Some companies do not analyze anything else, besides money-in, money-out analysis. A pity because then you can miss the behavioral values that affect money-in money-out ratios.

However, where companies can over-analyze money, individuals have a hard time understanding the intelligence and value of money.

Business Intelligence is the art of finding useful, actionable information in the existing data-volumes and then, utilizing this information to make decisions that can alter lives.

Even private lives in the micro level of finance.

There are lots of tips on how to stay afloat even through emergencies. For example, save 10% of your income every month. But does that really work for all of us, all the time? Doubtful.

Alternatively, the intelligence of money is being economical as a way of life.

To save is to learn to understand Business Intelligence of profits and losses:

  1. High Season, High Price. Low Season, Low Price.
    • Travel cheap – Buying tickets on certain days and certain times can save you up to 70% on flight tickets. Airlines earn the profits on the loaded dudes who buy tickets at full price on the wrong days. To fill the remaining seats, they then cut prices for the poor dude who must travel and are looking for a deal.

Be the dude and look for tickets at uncomfortable hours.

    • Buying certain items on certain days and certain times can save you quite a dime per year. Same theory as above. The profits are earned in the beginning of the sales season of whatever it is. Even fruits. Most people buy avocado at full price when avocados first arrive in the market. After a few weeks, most consumers have had a fill of avocados and the shops can’t seem to get rid of the avocados. Voila! Cheap avocados.

Shopping 60th January 20182. Companies use Discounts as a way to get rid of what can’t be sold on full price. It is not a favor to you. Learn your math for utilizing those coupons!If have combination promo codes and coupons, use the promo code that you can apply on the whole order, and full price first.

  • No Company is your best friend unless you have a friendly-price-contract. And even then. Learn to Compare. The biggest BI activity is comparison. Sales Now compared to last year/week/month etc. Compare your potential buys before you order.
  • You are your own core business. You generate the income and the profits. And unfortunately, you are also the expense. Understand your income inflow and outflow.
    • What costs you money? Is it something you must have for your core business to be productive? Can you automate it? Can you control the outflow?
      • Companies usually try to reduce employees and other volatile assets that need lots of maintenance. Can you?
    • What earns you money? Is it sustainable? Can it be optimized?

Put away as much as you can in cash every month. Once we learn the hack of saving, we can learn to invest the savings. This is recommended by most Financial advisors.

Don’t start to buy funds and shares until you are debt free and have a buffer saving.


  1. Cancel your credit cards
  2. Pay your debts.
  3. Save!
  4. Invest

After you have stashed away the ~10%, if you can, you can continue to save on the net income left. It is thrifty, and can be categorized as penny-pinching but the rewards are worth any name calling.




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!






Business Intelligence

The Business Intelligence of Time

Time is the most utilized and analyzed statistic. Prognoses: How much time do you think something will take? What does it cost per hour and is it worth it? How far are we traveling and how much time will that take? How much time do we have left and how does that translate into resources and cost? Time Management as cost conscious, long term, Business Intelligence.

Time as Courage: A thesis is presented where time is described as a facet of our best qualities. Lack of time in this case being presented as a tool of avoidance. Imagine Courage as an aspect of time. Courage says: “I have two hours on Saturday that I can spare but I want to spend these two hours on painting my nails, cleaning the bath tub and I have a nap planned at two o’clock sharp. Unfortunately, I will not be able to meet. Can we meet next Saturday instead?” Of course, it is not easy to say NO. To say NO may create conflict, dissatisfaction and other negative responses that we, peace loving, well functioning passive aggressive humans do not want to deal with. That is where courage comes in as one of the most important tools for time planning. The ability to courageously say: “No, I will not be able to attend to your need.

Time as Respect: Another theory presents Respect and time planning as two sides of the same coin. Apparently, respect for time generates respect in other areas of life. An example of time versus respect is the art of keeping promises. If a child requests for time and attention, and the parent promises to be available at a certain time and place, either to attend to an errand or to show support, showing up in time is a silent sign of a parent respecting the child. It is not enough to show up though, an attentive attendance makes the time spent even more meaningful. It also teaches the child the value of time. Killing two birds with one stone. Time as a tool of life and respect as a part of time management. When a leader or a friend is responsible for planning another person‘s time, time then has to treated as a product, a space, a resource, intellectual property. Just as clothes, shoes, rooms, books, cars, money etcetera. It is disrespectful to give away other people’s property without asking, without consideration and without consultation. If a person is constantly misusing other people’s time, that person adversely affects other people’s ability to plan their day, to fulfill their promises to others and themselves.

Time as Passion: A third theory aligns time with passion. Apparently, we spend most of our time on the things, people and causes that we feel passionate about. If you know your passions, and other people’s passions, then you know where most of the available time will be dedicated. Whatever we care about, whatever we think or feel is important, that which touches our values, our believes, or dreams, our inner selves, we are willing to devote time to that. However, as in all other matters, nothing lives a full life in a vacuum. To follow our passions, we need courage, respect and time. Time in this case as space for creativity. These three enable us to stand for our sentiments, in respect for ourselves. Additionally, courage and respect nudge us to respect others enough to let them choose how to divide their time and space between passion and duty.

Time as Effort or Energy: The fourth theory refers to time as the estimation of effort required to execute a task in relation to available energy or power. Estimation of time goes hand in hand with prioritization of one task or activity against another. If everything is critically important, life and death important, then nothing is important enough. All effort is a waste of time and resources. The complicated art of setting priority, investing courage, showing respect and engaging with passion in the prioritized is also the art of saving time. With the limited energy we all have, no an ounce of it should be wasted without passion, respect or courage. Or do I mean: With the limited time we all have, no an ounce of it should be wasted without passion, respect or courage?

Courage commands Respect, Respect cements Passion and Passion floods life’s endeavors with Energy

Someone quite appropriately coined the statement Time is Money. I counter with: Time is Space. Personal Space, Organizational Space, Collaboration Space, Financial Space. Time wasters are therefore space wasters. It should be possible and plausible to sue time wasters as the resource wasters they are. Nobody should be allowed to presume that they have the right to encroach or guzzle another persons time without consequences or repercussions.

 However, the person who has no respect for his/her own time cannot master enough respect for another’s time. Therefore, a leader who doesn’t understand the value of time cannot be entrusted with time planning.

What are your thoughts about Time Management and leadership?

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.


Business Intelligence, Data Nerds, GDPR

BI – All Feet on the GDPR-Gas Pedal

IMG-20171217-WA0000Business Intelligence and GDPR (General Data Protection Directive) cannot be divorced. They can almost be viewed as rivals in collaboration with each other for mutual existence. In business intelligence, Customer data behavior is the most valued analysis. This because companies have understood that customer is synonymous with business. Most companies with a vision are totally dedicated to customer Service. To serve a customer satisfactorily, a company needs to know and understand its customers.

For example, at the least, a bank wants and needs to know which of its customers saves a lot, spends a lot, buys shares, bonds etc to be able to make offers to specific customers that meet the specific customer’s needs.

Another example is a retailer that needs to know its customer’s preferences to be able to suggest the next buy, a suitable payment method, a pick up place, a delivery time etc.

All this means that data is saved in a vault somewhere and an analyst is looking into this vault every now and then. In mature BI organizations, there are tools for mining and analyzing this data as well as numerous analysts looking for clues in the data.

GDPR demands that we are aware and transparent about:

  • What we are saving about a customer
  • Where we are saving it
  • Who has access to the saved data
  • For what purpose we are saving the data
  • The length of time we are saving
  • The security of the saved data

The deadline is five months away. The reality is no one is done, let alone certain of what is expected of them for compliance. So all the feet that can be spared are on the GDPR-gas pedal. Mine too, though I cannot with all certainty be spared. Which leads to the question: Where did time go?

Tick-TockIs time simply a matter of counted hours that run through the fingers without stopping for a hello and goodbye?

Or is time the space between different things, different activities and different people? The space between eight hours of work, eight hours of sleep and vice versa?

The space when one arrives home, exhausted, ready to make a quick dinner for the household before dropping dead in the sofa or in bed.

The territory between choices. To go to bed or to log into the work computer and work two more hours? That is the question.

Is time the margins surrounding knowledge, perspective, respect, estimation and prioritization? Other aspects of life that create balance?

The space between:

  • The first time time you heard about GDPR and The first thing you learned about GDPR.
  • The first time you spoke about GDPR and the day you started planning for GDPR.
  • GDPR implementation, GDPR compliance and the maintenance of the Policy

Or is time all the virtues and demands warped into each other?