So-called ‘Dashboards’ are heard in our analytics driven world but what exactly is dashboard and why we should do it?
The easiest way to explain your data to someone else is actually to visualize it. Data Visualization could be considered as story-telling or how to tell your story to your audiences or as analysis tool to make you understand your data better.
To be able to visualize data, there is a very useful knowledge called ‘Semiology of Graphic’ which many seems to overlook and go to what chart I should use. Semiology of Graphic deals with how we can actually play with different aspects of the chart to reflex the differences in data.
Segmentation is to split the market into different groups – each group with similar characteristics. Why do we do this? Because, STP or Segmentation, Targeting, Positioning will help the company select the right segment to target with right positioning. You could not sell your product or services to all people on earth. For example, not all people will buy fruit juices, only those who concerned about health will be interested.
Have you wonder which Business Intelligence tool is the best for your organization? There are many BI Tools out there for you to choose from.
From Gartner’s Magic Quadrant for Business Intelligence tool in 2021, the top 3 BI Tools are Microsoft Power BI, Tableau and Qlik.
This blog compares the most popular tools : Power BI, Tableau and Google Data Studio for you. For us, we’d say use the right tool for the right objectives. Each tool has it own pros and cons.
If you are wondering how to start your Data Analytics Journey, Davoy.tech would like to present you Maslow Hierarchy of Data Analytics.
Step:1 Physical Need <> Data Driven Decision
You don’t need fancy models or advanced tools. The first step is to use data to drive your business decisions. For example, instead of just re-order the product your like, you might want to look at historical sales and the inventory level.
Step 2: Security <> Integrated Data
Next step is to get data integrated. This is the most overlooked stage to me. Most companies reported different sales revenue from different departments such as Sales, Marketing, Finance. This is due to different business logic and Silo. We should align on the data definition and data usage before moving up the Pyramid.
Step 3: Social <> Real-time Data
The world is changing fast. To catch up, you need fast data. Many companies are looking March sales in Mid-April as they need to wait for their staffs to pivot and report them. Wouldn’t it be better if we can see it daily?
Step 4: Self-Esteem <> Individual Data
To be able to run advanced models such as Customer Segmentation, RFM in Marketing 5.0, you need individual data points and more profile enrichments.
Step 5: Self-Actualization : Machine Learning
Finally, yeahhh we can use machine learning and Artificial Intelligence to enable your business for competitive advantage.
In reality, most companies, even MNCs, are still struggling with Step 1 or Step 2; some might be in Step 3 or Step 4. However, I can see lots of hypes around ‘AI’ and ‘Machine Learning’. I’d like to remind that we need to set our foundations strong before we can proceed to AI and ML.
If you want to set your foundations right, you can contact us at firstname.lastname@example.org
Getting started with Data Analytics
Data is garage in, garage out, good data and bad data, useless data and data with value. With digitalization, data is everywhere, raw data give contexts to help derive Information. Information gives meanings to become Knowledge and Knowledge gives insights to finally become Wisdom.
In simple words, we all probably have some data in different ways but how can we derive insights and value from Data? Before that, let’s understand the common problems of Data.
Common problems of “Data”
- Offline – Many data are in physical manual hardcopy forms
- Limited – Many useful and valuable data points are not collected
- Not integrated – Data that are available are in some files like Excel in different workstations
- Not usable – Data stored are also inaccurate, not logged properly or not updated
- Data hidden somewhere secure – Request to IT for data and reports to be generated. This often takes days or weeks and every time you need it.
With such common data challenges, the outcome is mostly basic charts and reports that requires heavy manual work and often results that are inaccurate, incomplete and not 100% trustable.
So, what can be done? Our 2-simple approach to get things started.
1. Set the “Data” foundation so data are available and fit for use
- Convert offline data to online, in other words digitalized hardcopy forms to online whether in database, Google forms or Excel files
- Collect data points that are of value: Plan, design and implement data collection process that are useful
- Integrated and consolidate them to a database management system such as Microsoft Access or MySQL or Data Lake/Warehouse.
- Put in place Data Quality checks by focusing on process, monitoring, profiling and governance for Data to be trusted.
- Schedule and automate data pipelines, refresh and alerts so it eliminates manual request or actions and get value from data isolated sitting somewhere.
2. Get actionable insights from trusted data
- Operational Report – Most reports are descriptive in nature and can only tell you what has happened such as sales last week. This is also reactive and operational in nature for measuring efficiency or performance.
This is the first and important step to understanding and getting value and use from your data.
- Advanced Reporting – As more data are available and integrated, you can explore data that are connected across multi-dimensions for analysis such as sales by locations by transaction volume and number of transactions over time.
More questions will be answered but also asked next. Reports will evolve to dashboard for exploration, benchmarking and decision-making.
- Actionable Insights – With multi-dimension analysis, apart from “What Happened”, you will be able to answer, “Why did it happen”. For example, why did sales drop in quarter, is it due to the products, stores, existing customers or new customers conversion. Actionable insights can now be derived, and proactive actions can be taken.
About 70% organizations are not getting value from data especially small to medium organizations and struggle with data challenges.
We believe that data can be easily unlocked and realized in today’s digitalization world.
In questionnaires, it is very common to see the questionnaire asking you “From 1 to 5, how much would you rate…?” (1 is totally negative and 5 is totally positive or vice versa).