Dashboards Are Dead, Long Live Contextual Analytics

In this post, I will be doing a deep dive into why contextual analytics should be the core of a business’s data-driven decision-making strategy.

Gone are the days where reporting was done off hundreds of printed pages, multiple punch folders, a calculator, and a pencil. The Monday morning panic of starting your reporting process just to get it done before the Friday presentation. We found ourselves in a never ending cycle.

Technology has exponentially developed and improved in areas we would least expect. You can now buy a fridge that can scan the items inside, determine if you need it replenished and then place an order online to be delivered to your door step. You would think that the never ending cycling of reporting would be broken, right? No! Printed pages have been replaced with files, punch folders have been replaced with data sources, a calculator has been replaced by excel and a pencil has been replaced with a data visualization tool.

What exactly is “a Dashboard”?

First, let’s make sure we all on the same page when it comes to dashboards. According to Microsoft(What Is a Data Dashboard | Microsoft Power BI), “A data dashboard is a tool businesses use to help track, analyze, and display data, usually to gain deeper insight into the overall wellbeing of the organization, a department, or even a specific process.” The ease of use of data visualization tools has created a world full of dashboards ranging from store sales to Olympic medals.

Why Dashboards won’t get you the insight you need

I often find myself questioning the objective a dashboard is trying to achieve. We are faced with overwhelming amounts of data, a few cool squiggly lines and (well let’s not forget) the KPIs – yes those boxes at the top with targets you have never heard of. After a few clicks around to understand the navigation, I begin exploring for the answers I need.

  • My store is declining in sales, why?
  • My employee turnover rate is high, why?
  • What’s that spike in refunds?
  • How’s is our performance vs last year?

Everyday, we need to find the answers in order to take action. A well planned and built dashboard still takes upwards of 20-30 mins for me to find these answers.

Now it all makes sense why users refuse to use dashboards and stick with number crunching on excel. They understand how they are getting the answers and have created workflows that work for them.

So, what’s next? Let me introduce you to your business’s new role model, your Chief Data Officers new favorite super hero, and your new best friend – Contextual Analytics.

What is Contextual Analytics?

I couldn’t find a definition that I thought best described contextual analytics. I decided to come up with my own – Contextual Analytics is instant insights generated from a combination of a business’s data sources. It provides a deeper understanding into the data you need to trigger action and make more informed decisions.

To simplify it, it’s instant answers you seek everyday to make data driven decisions. No more filtering a dashboard, navigating to where the answer is, noting the answer and then figuring out what’s next.

With businesses generating thousands of rows of data every hour, it is vital that they make fast informed decisions on up-to-date data.

Determining your Business’s Data Journey

Now, let’s outline the process of reaching the “sweet” spot of analytics – what we now refer to as Contextual Analytics.

Determine the data journey stage your business is currently in:

 

 

Data Journey

Stage 1: No Analytics

  • This is when your business makes decisions based solely off human intuition. Performance and the status of overall operations are eye balled.

Stage 2: Data exports

  • Businesses receive simple data in the form of Excel spreadsheets, CSV and access to APIs. There is a high chance that the business is sitting on untouched gold due to the reports not highlighting important information.

Stage 3: Minimal data reporting

  • The business has reached the stage where it received some essential reports such as monthly sales charts created in excel or numbers. Each user has their own best practice and way to do things.

Stage 4: Individual dashboards for individual roles

  • Dashboards are now built on visualization tools such as Tableau, Qlickview or Power BI.
  • These dashboards have been put together to visualize data for a specific role in a company. This requires high maintenance, has no standardization and is very role focused.

Stage 5: Contextual Analytics

  • Contextual analytics will be developed for a user to gain insights at the click of a button.
  • It will allow a user to make more informed business decisions and links back to a dashboard where a user can explore the data at a more granular level

How to get started with Contextual Analytics

Getting started with contextual analytics may be quite daunting to a beginner. However, it’s easy to set up once you know what’s required and what output you can get to drive better decision making.

I’ve listed the steps below to help you get started.

  1. Outline the data sources you have available. 
    • These may include the following:
      • ERP Systems
      • CRM Systems
      • HCM Systems
      • APIs
      •  Servers
      • Data from a Data Marketplace
  2. Define your business KPIs
    • Specify the exact data each user will require in order to achieve their assigned KPIs
  3. Assess the quality of your data
    • How much data cleaning do you need to do?
    • What data will be best to measure against the KPIs?
    • How often is the data refreshed?
  4. Start the Implementation:
    • Clean and model your data
    • Build the logic that will return the results you want
    • Determine the refresh schedule (when and how often do you want these insights)
    • Deploy for testing
    • User feedback sessions
    • Implement feedback
  5. Deploy to production and Maintain as needed

    Elements to consider during the implementation of contextual analytics

    There are some key elements to consider during the implementation of contextual analytics in any business:

    • Have a clear vision of what you want to achieve.
    • Based off your vision, define and cascade your KPIs across your business. Every team member should be aligned and heading in the same direction.
    • Make these insights available to every team member involved in the process. Team members need to understand the impact they have.
    • During the implementation, ask for feedback. It’s important that users are receiving the insights they need.
    • Aggregate your data to different periods, don’t get lost in the day to day performance.
    • Link your insights back to a pre-filtered dashboard, it allows users to drill into more details and will help them make more informed decisions.
    • Trust the process

    Use Cases for Contextual Analytics

    In this section, I am going to take you through an example of contextual analytics. You get to see how KPIs are set and how contextual analytics in the form of a simple email can drive immense value to your business.

    Let’s use my earlier questions as an example of contextual analytics:

    Setting up KPIs for your Business:

    Here, we get to see how a business would set up KPIs to help them understand business performance.

    My business has set its KPIs as follows:

    Store Net Sales:

    Calculated with the sum of daily store sales.

    • -15% day on day threshold to trigger alert
    • Data sources available for this KPI: POS System and Inventory System
    Employee Turn Over Rate:

    Number of employees who left / average number of employees over the last 6 months x 100

    • Threshold is set to 10%. Alert if Employee turn over rate exceeds 10%.
    • Data sources available for this KPI: Employee pay roll system and employee exit surveys
    Store Refund %:

    Calculated as the sum of daily refunds value  / the sum of daily net sales x 100

    • Threshold has been set to 5%. Alert if daily store refunds are above 5%
    • Data sources available for this KPI: POS system and Inventory system
    Store Performance Year over Year %:

    Calculated as the sum of Year To Date net sales / Same period net sales previous year x 100

    • Target is set to 30% Year On Year Growth
    • Data sources available: Cloud Data Warehouse with all historical data from the store.

    Using Email to Drive Action

    In this example, I’ve used emails to drive action from the contextual analytics received. There are many other ways to do this (such as SMS, push notifications .etc) but this will depend on how the business operates.

    Below is an example of how emails can be used to drive contextual analytics.

    Contextual analytics schedule: Every morning at 8am – sent via email.

    Your Daily Contextual Analytics is Ready:

     

    Hi,

    Here is your daily contextual analytics:

    Store Performance: -25%

     

     

    netsales graph

    Insights:

    South Store Sales ALERT

    Recommendation: South Store inventory is below the -15% threshold. South Store Inventory level is at 40% capacity. South Store cannot meet the demands of your customers.

    Employee Turn Over Rate: 60% 

     

    turnover rate

    Insights:

    ALERT: Employee Turn Over Rate is above your yearly average at 60%.

    Recommendation: 80% of the employees reported the reason for leaving was due to the relationship with the stores manager.

    Refund %: 12%

     

    refund graph

    Insights:

    ALERT:

    Stores refund percentage is above the threshold of 12%.

    Recommendation:

    65% of refunds were for the product ‘Fresh Full Cream Milk’. Inventory data – ‘Fresh Full Cream Milk’ was restocked 60 days ago.

    Store Performance Year On Year: +14%

     

    year growth

    Insights:

    Store performance is 14% higher than previous year.

    Best performing category: Pharmaceutical +214%

    Worst performing category: Alcoholic beverages -67% 

    Triggering Action through Contextual Analytics

    Now that we have read through our daily contextual analytics, we have the answers we need to trigger action.

    Action Examples:

    Store Performance: Call South Store manager, points to raise: Inventory Levels

    Employee Turnover Rate: Setup a meeting with HR Director and Manager. Points to raise: Reason for employees leaving.

    Refund Percentage: Call Store Manager, things to note: Fresh Full Cream Milk hasn’t been restocked in 60 days.

    Year on Year Store Performance:

    • Update CEO on store performance.
    • Call category manager to investigate decline in Alcoholic Beverage sales

    Time from opening your email to action: less than 5 mins. 

    Conclusion

    This truly showcases the power of contextual analytics and how it can directly drive performance in your business and ultimately improve your bottom line.

    Oh, and did I mention that these decisions aren’t being made from old data. This is your business’s performance from yesterday.

    Sounds to good to be true? To find out more be sure to request a demo. We will be more than willing to help your business on its data journey and ultimately reach the “sweet” spot of analytics.

    Check out our product, Vantage Point. Vantage Point (VP) is a no-code, click & go business acceleration tool which enables data driven decisions across your business. It drives interactivity across all parts of your organization by communicating value (KPIs), autogenerating tasks with cutting-edge ML/AI technology and enabling users to combine VP’s ML/AI recommendations with their own analysis. You can finally track the exact ROI impact throughout your entire business with Vantage Point.

    Get in touch by following this link

     

    Written by

    Marc

    Product And Brand Analyst

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