{"id":3213,"date":"2021-05-20T18:08:56","date_gmt":"2021-05-20T18:08:56","guid":{"rendered":"https:\/\/www.biconnector.com\/blog\/?p=3213"},"modified":"2021-06-04T14:42:09","modified_gmt":"2021-06-04T14:42:09","slug":"4-ways-to-level-up-your-data-analytics-beyond-dashboards","status":"publish","type":"post","link":"https:\/\/www.biconnector.com\/blog\/4-ways-to-level-up-your-data-analytics-beyond-dashboards\/","title":{"rendered":"4 Ways to Level Up Your Data Analytics Beyond Dashboards"},"content":{"rendered":"\n

The Untold Pain of Dashboard Users<\/h2>\n\n\n\n

Dashboards are a great way to get a sense of the current business scenario. However, that\u2019s not the only thing users practically rely on for decision-making.<\/p>\n\n\n\n

Gone are the days when creating a single source of truth, and building user-friendly dashboards defined the success of an organization\u2019s Data Analytics initiatives.<\/p>\n\n\n\n

There\u2019s no denying that dashboards are necessary, but that\u2019s 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.<\/p>\n\n\n\n

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. <\/p>\n\n\n\n

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.<\/p>\n\n\n\n

4 Ways to Go Beyond Dashboards<\/h2>\n\n\n\n

The good news is that some organizations have started moving beyond dashboards to help their dashboard users get rid of its limitations!<\/p>\n\n\n\n

In the context of the untold pains of dashboard users discussed above, let\u2019s take a quick look at the 4 ways to level up your organization\u2019s data analytics beyond dashboards!<\/p>\n\n\n\n

  1. Natural Language Processing (NLP) driven insights<\/li><\/h3>\n\n\n\n

    The leading Business Intelligence (BI) platforms such as Tableau, Power BI all have Natural Language Processing (NLP) capabilities, and are continually improving it.
    <\/p>\n\n\n\n

    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.
    <\/p>\n\n\n\n

    A good example of such a question is – How many units of product A were sold in NY city last month?
    <\/p>\n\n\n\n

    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.
    <\/p>\n\n\n\n

    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<\/a>, and leverage their intuitive data visualization features on your OBIEE data!
    <\/p>\n\n\n\n

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

  2. X Analytics<\/li><\/h3>\n\n\n\n

    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.
    <\/p>\n\n\n\n

    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\u2019t provide all the information required by the users for decision-making.
    <\/p>\n\n\n\n

    For example, the retail companies want to tap into information available outside of the organization\u2019s database, like facebook, twitter to name a few.
    <\/p>\n\n\n\n

    For these companies, it is essential to understand a customer\u2019s 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\u2019s image on it, and this offering cannot be suggested for a Boston Celtics fan!
    <\/p>\n\n\n\n

    The information – like the facebook groups a customer is in, the influencers followed by them etc are not stored in the organization\u2019s Data Warehouse, except for the links to a customer\u2019s facebook or twitter profiles.
    <\/p>\n\n\n\n

    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.<\/p>\n\n\n\n

  3. Proactive Decision-making<\/li><\/h3>\n\n\n\n

    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.
    <\/p>\n\n\n\n

    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.
    <\/p>\n\n\n\n

    Predictive analytics is also helpful in uncovering patterns that humans may not be able to find.
    <\/p>\n\n\n\n

    A major bottleneck for organizations to adapt to predictive analytics is figuring out where to start. It\u2019s always good to start simple and build complex models solutions when scaling up.
    <\/p>\n\n\n\n

    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.
    <\/p>\n\n\n\n

    Predictive analytics is the go-to option to help users find answers for questions about the business\u2019s future.<\/p>\n\n\n\n

  4. Decision Intelligence<\/li><\/h3>\n\n\n\n

    The end-goal of using dashboards is to uncover insights for decisions. How about building decision intelligence?
    <\/p>\n\n\n\n

    For insight-hungry users, any amount of insights could still leave a vacuum for doubt. They may wonder \u201cHave I gathered all the necessary insights to make the decision or is there a possibility for me to have missed out something?\u201c
    <\/p>\n\n\n\n

    With the right decision intelligence frameworks in place, the users can make decisions with increased confidence.
    <\/p>\n\n\n\n

    Building decision intelligence solutions require the collective efforts of end-users, analysts, data scientists and BI developers, and could take years.<\/p><\/ol>\n\n\n\n

    Conclusion<\/h2>\n\n\n\n

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

    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. 
    <\/p>\n\n\n\n

    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!
    <\/p>\n","protected":false},"excerpt":{"rendered":"

    The Untold Pain of Dashboard Users Dashboards are a great way to get a sense of the current business scenario. However, that\u2019s 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\u2019s Data Analytics […]<\/p>\n","protected":false},"author":19,"featured_media":3217,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[77],"tags":[161],"yoast_head":"\nData Analytics Beyond Dashboards - 4 Ways to Level Up<\/title>\n<meta name=\"description\" content=\"What's beyond data analytics dashboards? 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