Results are the consequence of actions. If we have taken measures, there will be results.
Generally, you will find three types of results after following the GIDAR Method:
Positive: Changes result in an improve against the goal baseline. In this case, we have improved against the Goal.
That is often, the ideal scenario, the golden path. We declare a goal. We use the information, and in the end, we meet the objective.
Negative: The opposite of positive results. We do change, but now we underperform against the Goal. Although you did not plan for negative consequences in principle, it contains useful information, like what doesn’t work to achieve the Goal.
Negative results can drive more insights than positive. These are the hypothesis breakers.
Irrelevant or Inconclusive: Now, after implementing your changes, there was none or minimal variation. This is the most complicated case to handle because you don’t really know if the actions affected the Goal good or bad.
There are, however, a few questions that can make you interpret the results:
Can you segment further the results?: Could it be that it actually had an impact on a certain cohort of users, or for a specific product?
Have you looked at the right data? This is more a due diligence a crosscheck.
Did you do enough changes?: In my view this is the most common case for irrelevant results. If the actions are producing minimal changes it is logic that there will be also minimal results. In this case, the best is to go back to the actions step and think about bolder changes.
Examples of the Three type of GIDAR Results
Positive Results
After running a marketing campaign we noticed an increment of 7% vs Goal.
The lead generation form produced 3244 contact vs 2487 that was the Goal.
Negative Results
The campaign ROI was 2% (The difference between the invested and the revenues).
Changes in HR policies have resulted in an employee churn higher than the Goal.
Changes in Customer Care led to a decrease in NPS of -3.
Irrelevant/Inconclusive Results
After changing the price of product A, there is no visible change in the sales of the product.
Changing process A didnt result in better or worse time consumed.
Results are your ultimate project goal, and it is the conclusion of the GIDAR Analytics Canvas.
Be ready for any kind of results and in many ocassions to iterate
If your changes are positive, you are in front of a success story. Document it, put it in the intranet or in the wall and let everyone know that your actions brought results to the company.
If they are negative, you have learnt something sometimes even more valuable, what does NOT work.
If no change dedicate time to think about the actions. Where they bold enough to produce a noticeble result. If so you might need to iterate with a new goal, if not talk to the sponsors and propose more noticeable changes.
Regardless the results, CONGRATULATIONS, you have already won you achieved what many couldn’t. Use Information and Data to produce business results.
Data analysis refers to the identification of patterns in data to extract conclusions and insights.
Data analysis is usually the role of a data analyst who will perform the activities needed to provide with actionable intelligence. Frequent activities done by a data analyst are data cleaning, data mining, statistical analysis, diagnostic analysis, regression analysis, data visualization, text analysis, cluster analysis.
Some of these are specialities of Data science and some others are more traditional data analytics techniques. Regardless, the objective of this step is to provide with insights that can be turned into actions (next step of GIDAR).
Elements of an actionable Data Analysis
Clear Goals
As stated in the first step of the method, Goals are intrinsic to businesses and they need to be clearly defined. Everyone involved must clearly understand what is that we are trying to achieve.
Make sure everyone is clear on where is the GOAL
Lack of clear goals is a frequent issue with data analysis and one of the main reasons of futile efforts.
Analysis Scope
Also, a frequent source of trouble is the scope. At the beginning of the data analysis, we need to objectively define what are the boundaries. What are the areas we are going to cover? What is excluded? What are the metrics and KPIs that you will use? Which countries are included?
Analysis Methodology
The data analysis methodology refers to the set of techniques and conditions that you will apply on top of the dataset. You need to declare the data analysis process that you followed. A good methodology includes:
Category of the data analysis: Descriptive Analytics, Predictive Analytics or Prescriptive analytics. You can have more than one.
Analysis technique: data mining, machine learning, statistical analysis.
Name of the data analyst.
Date ranges for the analysis and data.
Sources of data: Data Warehouse, Salesforce, Google Analytics, Shopify, etc.
Collected data: Ecommerce sales, customer purchases, lead report, campaign X.
Caveats or known issues: Excluded Data, missing countries, seasonality, etc.
Data quality: Is the data complete, accurate and unique?
Data Quality
Data quality is a common problem at the time of believing in the conclusions of a Data Analytics project. It is frequent that someone will raise the flag of non-reliable data along with the project (especially if they don’t like the outcome).
TO avoid this common pitfall is important to invest time on data quality. If you are planning to recommend changes you have to make sure that your conclusion is reliable.
Partnering with data engineers is always a good way, they can analyze the quality of the collected data.
Data Analysis Technique
Depending on the category of the analysis, descriptive, predictive or prescriptive analytics you will need to pick one or another analysis method as well as the analysis tools.
Python has become the main analysis tool for advanced analytics
For advanced analytics cases (prescriptive and predictive analytics) is common for a data scientist to use data analysis methods like data mining, machine learning and tools like Python or R.
There are a lot of good articles, discussions and even job opportunities around data science at the website Analytics Vidhya.
Dashboards are very useful to analyze data, not so much to present results
For descriptive analytics, the data analysis technique is more exploratory data analysis, descriptive statistics with tools like Microsoft Excel, Tableau or Power BI.
Analysis Output
Now that you have collected and analyzed quantitative and qualitative data is time to put it into a format that can be digested by everyone. I say digested because is common to see reports that are only understandable by the creator and that is a huge mistake. The report should be self-explanatory and contain clear insights.
The template below is from
How to produce a strong Data Analysis with Actionable Insights
Go back to your Goals
It is easy to get lost in the sea of DATA. Have a copy of your goals always in front of you. It is your north star and will put you back on track when you get lost.
Look at the Key questions
At the end of the step information (useful information), you should have collected a set of key questions like Why customers buy this or that? How can we measure the quality of the leads? What is the impact of a change in price?. These are good pathfinders for your data analysis.
Blend information with Data
Take the questions above and answer them with data or use the information to filter your data. For example, if you know that your sales at Christmas are always excellent you might want to exclude that period from the analysis.
Choose the right Analysis Technique
Chose the right data analysis tool, not all are born equal. If you are going for statistic, make sure that your dataset has statistical significance. Your data analysis skills could worth nothing if you choose the wrong methods.
When using visuals avoid complex visualisations like a scatter plot. They are very powerful but some get confused.
Dashboards are very good to monitor but for analysis, it is better to have single charts with captions. Less is more!
Master the Output
A great data analysis contains a scope, methodology, executive summary, visuals, captions, and conclusions.
Once you have all inside give it a beautiful final touch. People are more likely to pay attention if your output is nice. Make your conclusions and key charts with big and bold letters. If your output is PowerPoint use the 10, 20, 30 technique. It never fails.
Conclusion
There are many more things to say about data analysis, it can get dense, complex and long but it can be much lighter and easier if you rely on the previous 2 steps of the framework. Focus on your goals and use the information.
After defining clear Goals and collecting as much information as possible (clarifying key questions), you will have a good idea of the data that you need. Here you want to be methodical. That is the technical part that requires extracting data, access to data sets, databases, build data models and data cleansing. That means that even if you or your team are not in charge of this part, you need to prepare a full brief.
What data do you need?
In the previous step, information, you should have collected the key questions to answer. They are the starting point for the data collection.
You need to write down the questions if you haven’t already and figure out which data you need to answer them.
Don’t be shy, take this initial list as a wish list, so put everything you think could help answer your key questions.
How to collect and store the data
In a spreadsheet, list all the questions, data sources and fields, you need to answer the key questions you gathered.
The next step is the data gap analysis. For this section, we can use any validated gap analysis technique. If you want to keep it simple, you can start with, availability, accessibility and DQI (Data quality Index).
Availability refers to the mere existence of the data; do we have competitors data? It is usually a binary result, either yes or no.
Accessibility is a step after confirming availability, and it answers the question, can we access the data? Here you might get yes, no or yes but. For example, yes, but we cannot use specific fields due to privacy.
DQI or Data Quality Index. That is a quality of measure for the data we will use, and it answers the question. Can we rely on this data? DQI can become complicated, but I recommend you to make sure that the information is accurate, complete and unique if you want to know what these mean have a look into the Introduction to Business Analytics course. And if you don’t have much time, look at the data quality charts presented here.
Examples of Data points
Examples of Data for “Jumpy Shoes”:
Total sales of “Jumpy Shoes” by hour of the day, day, week, month, quarter of year.
Total sales of “Jumpy Shoes” by model
Total share of “Jumpy Shoes” sales by model.
Customer that bought “Jumpy Shoes”
Demographics
Psycographics
Price history of “Jumpy Shoes”
In a more DATA friendly format:
Model
Date
Time
Price
Size
Channel
Oder ID
Customer ID
Shoe Gx
20/03/2020
1:23pm
76.5
38
Web
1214914
Customer1
Shoe Gy
24/03/2020
9:34m
49
42
In Store
85895798
Customer2
Example of a table with sales data
Customer ID
Age
Gender
Post Code
Number of Purchases
Last Purchase
Customer Lifecycle Value
Customer1
24
Female
4057
3
19/01/2020
146.74
Customer2
44
Male
4914
1
20/05/2019
56
Example of a table with Customer data
Final Remark
By the end of this step, you should have collected and prepared the data you will use for the analysis.
Information or better said Busines Information is an essential step in the GIDAR Analtyics framework that is sadly commonly ignored by Analytics teams.
The problem when you don’t use Business Information
When you get a Business Analytics request, the usual course of action for Data/Analytics/Insights teams is to jump into finding answers immediately. You send analysts and engineers to collect and analyze data when there is already much-uncovered wisdom.
Being Busy is not always being successful
What happens when you do that is that you neglect a ton of beneficial knowledge that will help you complete the project in less time and with more chances of succeeding.
What is Information in the context of GIDAR?
In this case, business information refers to context, known facts, know-how and experience from stakeholders, and subject matter experts. In one word, WISDOM. There is much wisdom in any company that for one reason or the other (called Knowledge Management in modern business) not always is surfaced.
How to acquire Business Information?
Obtaining Business Information is mostly asking, asking and asking. You need to wear your Sherlock Holmes detective hat and interrogate key business people to extract critical knowledge.
Find the Wisdom around you. It will save you plenty of time and you will be set for success.
This phase’s outcome: you want to have information that helps us frame the right questions for the analysis step.
For example, if the objective is to increase sales (refer to Goals to find a detailed one), you shall approach the sales managers or the experienced salesforce. Then you will and ask them about their ideas on increasing sales or things like our audience.
This step’s objective is to find the existing information and knowledge before anything else.
Business Information Sample Questions
Some common Business Information questions people make:
What do you know about the Goal?
What is the business model?
What is marketing doing?
What are the usual complaints of the customers?
Has it been done before? How did you do it last time?
How will you do it?
Where can I likely fail?
Who knows a lot about this topic?
Who is responsible for the actions?
Is there any restriction or limitation?
Hint: Make sure you give credit to all your sources of information.
Why is Information sometimes more critical than Data?
I assure you that there are people that know a lot more than you in your company. Ask them! Don’t run the project in isolation. They will love to help. They will gift you with hypothesis and ideas to complement your analysis and things that you can straight include in the actions section. You don’t need to do Analytics to make common sense.
Outcome: Key Questions
By the end of this step, you will have a list of critical questions to answer with data in your analysis:
Why “Jumpy Shoes” sales always spike in March?
Where are our customers dropping?
Who are the competitors of “Jumpy Shoes”?
How does the price of “Jumpy Shoes” impact their sales?
What can we and what can’t we do?
Do not confuse the critical questions with the Goal. The goal remains the same, but the Key Questions are a great vehicle to produce a robust analysis and actions.
Goals are intrinsic to any business, even if we do not explicitly declare them, there are always goals behind any activity. A known problem in Business is, however, the inability to articulate Business Goalsproperly.
Business Goals are the first and essential step in the entire GIDAR framework. Without purpose, there is no project. If after your stakeholder’s meeting or Business Goals Workshop you don’t have a clear understanding of what you are trying to achieve, you shouldn’t probably be spending resources. In that case, it would help if you revisited your company vision, mission and strategy.
The best way to set a successful Goal is to use SMART goals.
Business Goals must be:
● Specific: If possible numeric
● Measurable: You should be able to track it
● Achievable: Must be something doable (Avoid x10’s)
● Relevant: Have to bring a positive impact.
● Time-bound: Must have a beginning and an end in time.
One more thing your goal should contain is as much as possible a baseline. If you are to increase sales, you should know where your sales were last year or last month.
The more thoroughly you define these steps, the more likely you are to succeed.
Examples of poorly defined Analytics Goal:
● Increase customer NPS (Net promoter Score)
Example of well defined Analytics Goal:
● Increase average customer NPS from 4 to 25 – Specific
● We will use the customer surveys to obtain the data and process it into a monthly report. – Measurable
● We are confident to reach 25 because that is the industry average. – Attainable
● Customers with NPS above 20 spend, on average $100 more per year. – Relevant
● The analytics part of this project will be completed between 20 and 25 days. After delivering Insights and recommendation, we expect four months to implement actions and another 60 days before we measure the results. – Time-bound
Examples of poorly defined Analytics Goal:
● Increase sales
Example of well defined Analytics Goal:
●Increase sales for “Jumpy Shoes” Category by 5% from 10,000 units/month to 10.500 Units month. – Specific and with a baseline
● We will use the ERP data to measure the increment and Marketing Data to assess the campaigns’ impact- Measurable.
● The 5% is based on the industry forecast for these products next year and the competitive market share.- Attainable
● “Jumpy Shoes” category A represents 20% of our global sales. Moreover, a 5% increase will enable the launch of a new shoe category – Relevant.
● We will measure results on a bi-weekly basis during the next six months. There is the first milestone of +2.5% on month three and a final report after month 6.
Note how we have gone from a broad question to a more specific query. When you have worked out such detailed Business Goals, it is more evident for all parties what they are trying to achieve.
Business Sponsor and Analytics teams can now move to the next phase of GIDAR, Information.
