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.
In 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.
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.
This post is triggered by the January financial woes. Everybody, or most us at least, agree 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:
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.
2. 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.
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 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.
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:
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.
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.
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!
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:
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.
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.
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 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?
Is 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?
In my opinion, the essence of Business Intelligence (BI) is not the what but the why. You have heard that before, so it will annoy you to hear such a cliché, especially concerning as serious a matter as BI.
Bear with me, please.
Of course, BI is different for different businesses depending on the level of maturity, the market share and the revenue. Mature, well established businesses probably need BI for the complex purpose of keeping the customers they already have and where plausible, for attracting new customers. It is called Improved Sales.
New businesses need BI for the sole purpose of establishing a presence in a new, probably unknown, and probably occupied market. What we now call BI has existed in many formats from as long as time is ancient. Market Research for instance:
What do you want to sell? What needs selling?
Is there a Buyer?Is there a need? Why do you think there is a need?
Does what you want to sell already exist? If yes, how much of it exists? Why do you want to sell it anyway? If no, why isn’t any other clever bugger selling it?
Location? Have you found a perfect location? Why is it perfect?
How about the competition?What do you plan to do differently? Why do you want to do anything different?
You see the whys? It is a step by step progression from what to why of course, but the most actionable knowledge comes when you have the answer to the question Why.
Here is a breakdown of my theory: Say you ask for a report of the potential customers in the vicinity of your new business venture. Say that after a lot of research and consideration, you decide that your new business venture should be Selling Beautiful Handmade Wallets. You will be designing the best wallets at home and and retailing them in a small Online store in a back alley behind the online garage in the city called Online Nowhere.
Number of potential customers = 1,4 customers
Promising Buyers = Married Women
Expected Revenue = 10,2
Currency = SEK
You even get an example customer:
Customer Number = SE4321 (Swedish Customer)
Customer Name = Lim Pan
Customer Payment method = Credit Card
Customer Address = 43 Trinity Stockholm
Customer Gender = Female
Customer Age = 37.5
Customer First Order = 12th June 2014
Customer Most Recent Order = 12th June 2017
Customer Average Order Amount = 557
Customer Currency = SEK
Customer Marital Status = Married
Customer Family = Two Children
Imagine you get the above simplified report including the detailed information on a specific customer, what catches your attention? Is it the average amount, the date of birth, the gender or the payment method? The more urgent question is, Why do you want to know anything at all about a specific customer? Is it for marketing, is it for Service improvement or is it for analysis of customer behavior?
What is Service improvement for you?
I would like to suggest that the best way to utilize BI in this case is to find out:
Why you entrust your life to a wallet?
Why would anyone want your beautiful wallets?
Don’t get insulted, they are beautiful wallets! But, there are multitudes of ugly wallets that do the job! So, is it that your beautiful wallets function better than the ugly ones? Is it possible that your beautiful wallets are cheaper or pricier than the ugly ones? Why is that good? Is it good for you or for the intended customer?
Data says that the potential customer pays with a credit card. Why Credit Card?
Could the potential customer be persuaded to pay by other means?
Why is the potential wallet buyer a woman? Do women use wallets?
Data says, there are 1,4 potential customers in Nowhere. Why 1,4?
How many people live in Nowhere? Why don’t the space occupants need wallets?
Why the best wallets in the world? Why not cats and dogs? Why not hair pins?
Why retail? Why a small Online store in a back alley behind the online garage in the city called Online Nowhere? Why not a big wholesale beside Best Priced Wallets Online?
If you cannot gather enough data or enough gossip to answer most of these questions, and more, you can kindly be accused of gambling.
With BI, you do not always have to have a huge complex databases, the latest BI tool, or a team of BI experts running around their tails recreating age old KPIs to send out age old reports.
BI is a way of life.
It is the self preserving choice to not jump on any train without asking where it is going.
The cool-headed decision to not spend a dime without checking how many dimes you have left.
The safety precaution of not walking a dark alley alone.
The SMART goal that needs a background, a baseline, an investment, a commitment and a future.
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:
Online analytical processing
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:
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.