As in any project, and particularly in large companies there will be a considerable number of people involved in a mission. Below you can find a suggested timeline of events linked to the steps in the GIDAR analytics framework.
Request / Idea / Business Case / Inquiry
You or your managers have an idea and are wondering how to put it in motion using Information and Data.
Goal Meeting/Workshop
This is generally done with the project or goal sponsor. They need to be present and by the end of the session you should have your 2 o 3 goals very well defined.
Stakeholders / Subject Matter experts Interviews
Extract all the information that you can from the people that knows
Data Gap Analysis
What data do you need to complete the analysis and answer the key Questions?
Data RACI
Who will get which data? In many companies data tend to be scattered and is owned by several parties. Define who is Responsible, Accountable, Consultant and Informed in the Data Collection.
Analysis Showcase
Present to sponsors and stakeholders your findings. Make clear that this is not the end of the work.
Actions Brainstorm
Put together all people involved and do not leave the room/call until at least one clear action is decided.
Result Circulation
After taking actions present the changes as defined in the Goal Time bound.
When an individual or an organization wants to fix something, test a new idea or embarks on any mission, normally what they are looking for is to change something.
Change and Disruption
To produce change, you need to take actions, because if no steps are taken, things will continue being as they were until you or your business get disrupted. Disruption is something that you don’t necessarily want to see in your life or company or let’s better say at least that if you get disrupted, you want to be somehow ready.
Today, being in constant change is a must for most of us (look at 2020 if you want some examples).
The most distinct feature of the GIDAR Analytics Framework is the insistence on Actions, on producing change.
Who is Responsible for Change?
Suggesting what to do next or how to change has been traditionally out of the scope of Business Analysis and Insight teams. Unfortunately, business sponsors will trigger changes using less, let’s say, scientific approaches like HIPO or eventually not change until disruption comes.
Fundamentally, we need to take actions to meet our goals. The base for these actions will be the Information, Data and the Analysis that we did in previous steps.
Action may not always bring happiness, but there is no happiness without action.
Benjamin Disraeli
Types of Action
In a normal situation (sometimes also in extraordinary circumstances) there are three types of action:
Removing something that is not working: Closures, laid offs, cost-cutting, etc.
Replicating something that worked well before: Products, Campaigns, Offers, Teams.
Experimenting with something new: New technology, a new type of product. Something that we haven’t done before.
Inducing Action
A critical human factor of the GIDAR Methodology is proactivity. It is crucial that you come to the Actions workshop with ideas or suggestions, and that you approach this session as a brainstorm.
Regardless you are an expert or not in the topic; you have business domain knowledge, you are Junior or Senior, after collecting Information, Data and doing Analysis you (your team) must have some ideas.
The second part of this step is to get more initiatives from the sponsors/stakeholders.
Finally, assess which one of them are feasible and which ones we cannot do now.
By the end of this step, you need to have one or more actions that will be put in motion to meet the goal.
Some (basic) examples:
Increase, reduce prices
Change the opening hours
Implement new policies
Change a process
Target a new segment
Launch a campaign
Add new products
Remove products
Launch a new sales channel
Implement a lead acquisition tactic
Summary: No Action, No Change, No Result
Actions differentiate a good Business analysis from an excellent one.
Actions are also the most challenging step of the GIDAR framework because you need to produce one of the hardest things for humans: Change. We are creatures of habit, and we hate changes.
Changes at work usually mean “more work” or simply moving out of the comfort zone. Get buy-in from stakeholders and decision-makers. If they are reluctant, Produce accurate estimates if possible on what the change could result, back up every suggestion with your analysis, so there is no space for ambiguity.
Your objective in this step is to bring everyone on board. When you change your direction, you frequently find waves ahead, and everyone should row in the same direction.
The best part is that you are only one step away to see Results.
The hype of Big Data Analytics has stormed by now quite a lot of Businesses. Only by looking at the amount of literature generated around the topic you can figure out how relevant has become.
Source: Knowledge Graphs and Big Data Processing by Valentina Janev, Dea Pujić, Marko Jelić, Maria-Esther Vidal1
Companies quests for 360 customer view, next best action algorithms, self-service BI, AI and alike Analytics Projects had mostly failed.
Based on a prediction from Gartner2, through 2022, only 20% of analytic insights will deliver business outcomes.
But what are the reasons for what a well funded, sponsored and envisioned project can fail to leave companies not only with a few million less in the bank but also with an empty promise?
Unrealistic Expectations
Analytics requests often include “mission impossible” questions that not even God is ready to answer. Linked to that, companies’ immediate need for seeing returns on their analytics projects concludes with upsetting results. As much as Data & Analytics is a powerful tool, it needs time and patience.
Half of surveyed Senior leaders think that Marketing Analytics did not have the expected influence for the business. Source Gartner3
There is no magic button that you can press and tell you what to do with 100% of accuracy. If that would have existed, the COVID pandemic, for example, shall have never happened.
Weak Promises
We have the system that your company need; this is the future. These are common vendors claims. The fact is that they need to sell and they don’t doubt to offer you something that they know will not work.
Big Data” and other buzzwords created by sales/marketing/BI folks have completely bamboozled companies into creating new teams and positions. “Big Data” is not just a large data set. Around 90% of the professionals pitching Big Data and Social Analytics do not have a grasp of this area.
Overengenieered, unusable products are now part of companies infrastructure. They were expensive, they don’t work, and it will be even more costly to dismantle them.
Lack of Methodology
The fact that a company have the money, the technology and even the people doesn’t mean that they will succeed using analytics. That is particularly true for corporations with complex structures. The disconnection between functions, divisions, departments, countries, time zones and most crucially, objectives is widespread.
This part is were tools like the GIDAR Analytics Canvas can help put everyone on the same page.
Companies are Not Ready
That includes the large group of:
We don’t have the skills
We don’t have a budget
We are busy with other priorities.
And probably the worst one, We don’t have data!. (Yes some companies haven’t collected and sorted their data and yet want to do AI).
Conflicting course of action and Data quality are big problems at the time of using data to inform decisions. Source: Gartner5
According to Venturebeat only one out of every 10 Data Science projects actually makes it into production6.
Change Management
We spoke about technology, processes, and other known issues, but let’s not forget that is people who do analytics in the end. In many cases (self-service BI is a flagrant one), employees are thrown into a buggy system containing wrong and incomplete data and are asked to solve all company problems.
Employees stress levels based on Change parameters. Source: Gartner7
Companies must understand that we are in a speedy period of humanity and that people need time and support to embrace change.
Closing Note
The Big Data Hype will not go away anytime soon. It will probably be substituted by other buzz words like Artificial Intelligence or Machine Learning and vendors will still market it as Saint Grial: The capability that will make your company take over the rest.
Make sure that you have the right foundations (Goals, Culture, Skills) to make your next analytics projects a success.
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.
