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4 Ways to Level Up Your Data Analytics Beyond Dashboards

The Untold Pain of Dashboard Users

Dashboards are a great way to get a sense of the current business scenario. However, that’s not the only thing users practically rely on for decision-making.

Gone are the days when creating a single source of truth, and building user-friendly dashboards defined the success of an organization’s Data Analytics initiatives.

There’s no denying that dashboards are necessary, but that’s just not sufficient for decision-making from an end-user perspective. The end-users deal with much more dynamic scenarios that dashboards don’t have an answer for.

The major drawback of dashboards is it is limited to the information which the users (or the dashboard creators) foresaw they need for decision-making. 

However, in reality, the dashboard users face many uncertain scenarios that force them to look for information beyond the dashboards (and supporting reports) in order to make confident data-driven decisions.

4 Ways to Go Beyond Dashboards

The good news is that some organizations have started moving beyond dashboards to help their dashboard users get rid of its limitations!

In the context of the untold pains of dashboard users discussed above, let’s take a quick look at the 4 ways to level up your organization’s data analytics beyond dashboards!

  1. Natural Language Processing (NLP) driven insights
  2. The leading Business Intelligence (BI) platforms such as Tableau, Power BI all have Natural Language Processing (NLP) capabilities, and are continually improving it.

    When the user has a question, and doesn’t find its answer in the dashboard or reports presented to them, they can simply type the question in natural language (or conversational tone), and get the answer.

    A good example of such a question is – How many units of product A were sold in NY city last month?

    The leading BI platforms don’t require any complicated setup to leverage the NLP feature. All the users have to do is just type-in the question and gain insights.

    Are you using OBIEE (and OAC/OAS) and facing challenges in gathering insights due to absence of data visualizations? You can connect Power BI and Tableau to OBIEE via BI Connector, and leverage their intuitive data visualization features on your OBIEE data!

    The next time your dashboard users complain about its limitations, just ask them to try asking their question directly to the BI platform!

  3. X Analytics
  4. X analytics is a term coined by Gartner to refer to the analytics of different sets of unstructured data, in multiple formats from a variety of sources.

    A major drawback of dashboards is – it primarily relies on the single source of truth created by the organization. However, this single source of truth doesn’t provide all the information required by the users for decision-making.

    For example, the retail companies want to tap into information available outside of the organization’s database, like facebook, twitter to name a few.

    For these companies, it is essential to understand a customer’s interest to get an idea of their taste, in order to suggest offerings to them accordingly. A GSW fan is more likely to buy a tee with Stephen Curry’s image on it, and this offering cannot be suggested for a Boston Celtics fan!

    The information – like the facebook groups a customer is in, the influencers followed by them etc are not stored in the organization’s Data Warehouse, except for the links to a customer’s facebook or twitter profiles.

    It is not possible to store such information in a Data Warehouse as well. In such cases, the users must have the ability to analyze such data from outside the single source of truth while successfully blending both the data.

  5. Proactive Decision-making
  6. A common thought in the mind of every decision-maker is what will be the future of the business, because they have a huge responsibility to plan ahead proactively, rather than being reactive.

    Predictive analytics comes to their rescue. However, most dashboards present only the hindsight view of the business, but never give an option to foresee the future.

    Predictive analytics is also helpful in uncovering patterns that humans may not be able to find.

    A major bottleneck for organizations to adapt to predictive analytics is figuring out where to start. It’s always good to start simple and build complex models solutions when scaling up.

    To implement predictive analytics, the BI developers can start with Simple Moving Average (SMA)s on key metrics and move on to complex models based on user needs.

    Predictive analytics is the go-to option to help users find answers for questions about the business’s future.

  7. Decision Intelligence
  8. The end-goal of using dashboards is to uncover insights for decisions. How about building decision intelligence?

    For insight-hungry users, any amount of insights could still leave a vacuum for doubt. They may wonder “Have I gathered all the necessary insights to make the decision or is there a possibility for me to have missed out something?“

    With the right decision intelligence frameworks in place, the users can make decisions with increased confidence.

    Building decision intelligence solutions require the collective efforts of end-users, analysts, data scientists and BI developers, and could take years.


A few years ago, businesses with solid BI dashboards had a competitive edge. However, in the recent past, dashboards have become the new normal. 

All the 4 ways to move beyond dashboards rely heavily on Artificial Intelligence technologies. Hence, businesses moving past dashboards by implementing AI solutions will have a competitive edge in the coming years. 

Though it is extremely hard for businesses to build entire AI (or ML/DL) solutions, they can easily leverage the solutions provided by innovative startups in the AI space!

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Tags: Data Analytics

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