3 items tagged "best practices"

  • BI dashboards: best practices

    BI dashboards: best practices

    If you want your business intelligence dashboards to succeed, you'll need to make sure you follow these best practices along the way. Here's what to know.

    Business intelligence (BI) dashboards are increasingly used by companies around the world. If you use one or intend to, knowing some business intelligence best practices can help you avoid pitfalls.

    Here are 10 business intelligence best practices to follow as you design a dashboard and choose which information to display.

    1. Identify your reporting requirements

    BI dashboards make it easy to gather statistics and turn them into reports. Before diving into that task, clarify what to include in the review and which departments will read it.

    For example, the accounting department likely needs substantially different metrics than your customer service team. Get confirmation of the necessary details and the intended audience first to save yourself from wasting time on extra work and including irrelevant information.

    2. Choose a dashboard to meet your needs

    There are several kinds of BI dashboards on the market:

    • Strategic: These aggregate crucial details about your organization’s current status and health while highlighting opportunities for expansion.
    • Analytical: Dashboards that show data variables across a set timeframe and help you spot trends.
    • Operational: Choose these dashboards if you want to focus on key performance indicators and real-time operations changes.
    • Tactical: Mid-level managers most commonly use these dashboards, which give deep dives into a company’s processes by showing weekly metrics and trends.

    Find business intelligence dashboard examples based on the category above that most closely matches your needs before investing in a solution. Doing that increases the chances of feeling satisfied with your investment.

    3. Design your dashboard to minimize distractions

    One of the most useful dashboard design best practices to follow involves getting rid of superfluous information. Make your dashboards useful for everyone by following the five-second rule. Pick the dashboard’s content carefully so that anyone looking at it can get the details they need in a few seconds.

    Scrutinize the information and verify that each graphic or text snippet serves a well-defined purpose. If it doesn’t, take it out. Adding too much data to your dashboard could make it more challenging for people to focus on the parts that matter most to their work.

    4.  Call attention to relevant numbers

    Some viewers may appreciate graphic helpers that highlight statistics. For example, one of the Power BI dashboard best practices Microsoft recommends for its product is to use a card visualization for numerical figures.

    If you use a different BI product without that feature, consider other ways to help numbers stand out. For example, you might put them in a bright color or increase the size of the figure compared to the surrounding text.

    5. Restrict dashboard access to authorized parties

    Working with a BI dashboard also means engaging in the appropriate security measures. Some content management systems allow you to only give administrative capabilities to people with the right credentials. You could take the same approach with your BI interface.

    Think about setting role-based privileges based on whether a person requires editing privileges for their work or only needs to look at the content. Adjust or remove an individual’s access as appropriate, such as when they get promotions or leave the company.

    Also, encourage everyone to demonstrate good password hygiene, including using a different password for each service and never sharing credentials.

    6. Arrange your data according to the inverted pyramid model

    News professionals understand the inverted pyramid approach well. It involves mentioning the most important information first in an article and devoting the most overall space to it. The less-crucial details appear near the end of the piece and may only encompass a single paragraph.

    You can follow dashboard design best practices by letting the inverted pyramid model dictate how you show the data. For example, feature the main details inside the largest panes or sections.

    7. Select the right kind of chart

    Charts can be ideal for helping executives deal with the challenge of interpreting data and using it for decision-making. You’ll get the best results when you pick a chart type that aligns with your needs and the type of data presented.

    For example, line charts work well for showing trends over time, while pie charts let you show how single categories relate to an overall value. You might also use a vertical bar graph to help users compare differences side by side. The main thing to remember is that no one chart is the universal ideal.

    8. Include the most important information on a single screen

    If you’ve spent time checking out business intelligence dashboards, it may have become obvious that all the crucial details are immediately presented and don’t require swiping between several screens. Allowing people to see the essential material on one screen is the best approach because it increases clarity and helps you stick to your main points.

    Think about how some of the people who see the content may have packed schedules and might feel eager to get the information they need without wasting time. We discussed earlier how you should cut out unnecessary information to prevent distractions. This is a related point, but it’s a tip that encourages you to think about which data to show first while remembering your audience’s requirements.

    9.  Consider optimizing your dashboard for mobile users

    Web designers know how important it is to design content for mobile phones, especially since many people view it on those devices more often than their computers. One of the related Power BI best practices is to tweak your dashboard for those who look at it on smartphones.

    Doing that involves switching the content from Web View to Phone View in the dashboard upper-right corner. You’ll only see that option as the dashboard’s owner. While in phone view, you can adjust the layout so that it appears differently to phone versus computer users by rearranging tiles or changing their sizes and shapes.

    If you use a different product, determine whether it has a mobile-friendly option.

    10. Display data in the proper context

    As you design your chart, pay attention to how factors like the relative size and color of content on the BI dashboard could lead people to draw certain conclusions, not all of them necessarily correct. Ensure that you use labels and source citations to help people see the data in the right framework and not get the wrong ideas.

    You’ve probably seen at least a few dashboards that looked fantastic at first glance but later realized they did not offer enough context. In that case, you probably came away with some questions and uncertainties. Including reference points for the statistics and charts on a dashboard helps viewers feel confident while digesting the material.

    Tips to guide your efforts

    These business intelligence best practices will help you get the most out of any dashboard you purchase and use. Remember that it’s also valuable to devote sufficient time to training yourself or your colleagues on how to use the tool. Each BI on the market has different features and layouts.

    The more thoroughly you get acquainted with them, the easier it’ll be to get the results you want.

    Author: Kayla Matthews

    Source: Smart Data Collective

  • Getting the most out of your data with analytics: three best practices

    Getting the most out of your data with analytics: three best practices

    Data has three main functions that provide value to the business: To help in business operations, to help the company stay in compliance and mitigate risk, and to make informed decisions using analytics.

    “Data can have an impact on your top line as well as your bottom line,” said Dr. Prashanth Southekal, CEO of DBP-Institute in a recent interview with DATAVERSITY®.

     “Just capturing, storing, and processing data will not transform your data into a business asset. Appropriate strategy and the positioning of the data is also required,” he said. Southekal shared best practices for analytics and ways to transform data into an asset for the business.

    Lack of Analytics Success

    Gartner predicts that by 2022, 90 percent of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency. “Given that the organizations across the world are looking at ways to glean insights from analytics and make good decisions today, not many companies are very successful in analytics,” he said.

    According to a recent McKinsey survey, most companies understand the importance of analytics and have adopted common best practices, Southekal remarked. Yet fewer than 20 percent have maximized the potential and achieved advanced analytics at scale. With this in mind, Southekal compiled a list of analytics best practices, using his experience working with successful analytics projects, projects with challenges, and those that fail.

    Data Must be Usable

    Data from texts, video, audio, and other similar types of data are in an unstructured form when the data is initially captured, he said. The process of conversion from a raw state into a processed format creates value because it becomes usable for insights and decision-making

    Intuition vs. Data

    “The real substitute for data is intuition,” he said. Insight for design-making can come from data or from intuition, so in companies where data literacy is poor, intuition will prevail over data in decision-making. Users no longer need to rely on intuition when they realize they can rely on better decisions made with good data.

    Top Three Best Practices for Analytics

    • Improve Data Quality: Southekal defines analytics as the process of gaining insight by using data to answer business questions. Unfortunately, Data Quality is very poor in most business enterprises, he said, and poor-quality data cannot provide reliable insights. Data Quality will continue to remain poor under the current business paradigm, where businesses are constantly evolving — both internally and externally — in response to changing market conditions. Mergers and acquisitions require internal and external changes to often disparate data sources and systems. “Data Quality is a moving target and you can’t assume that if your data is good today, it will continue to be good, even after two years.” One option is to wait for the quality to improve over time, but in order to move forward in the immediate future, Southekal suggests creating a work-around with data sampling, acquisition, and blending of data from external sources, as well as investments in feature engineering.
    • Improve Data Literacy: More companies are recognizing that Data Literacy is critical to their future success with digital technologies and data analytics. Poor data literacy ranks as the second largest barrier to success among Gartner’s survey of Chief Data Officers, he said, who feel increased responsibility to ensure that data is easily available to stakeholders to use for all their daily operations. Building a data culture and investing in data literacy can show great benefits.
    • Monetize Data: “Go beyond insights and make the picture a little bit bigger by talking about data monetization,” he said. One effective way to monetize data is to look at data products. Also, monetizations entails reducing expenses, mitigating risk, and creating new revenue streams with data products.

    Data Products

    In most places, he said, analytics initiatives are run like projects, with a fixed start and end date and a specific purpose. The focus and the resource commitment inherent in project-based thinking is good, but Southekal recommends also thinking about analytics as a potential data product.

    “LinkedIn is a data product. Bloomberg Solutions is also a data product. You can even build a report which gives you a sales margin and call it a data product.” The objective of building data products is to have scalable and long-term solutions, instead of a short-term solution that ends with the project. “Analytics as a strategic endeavor has to be a long-term initiative, so you have to treat analytics as a data product delivery mechanism, not just as a project initiative.”

    How to Build a Data Product

    He suggests considering the business as a network of customers, employees, vendors, and partners; look at business as an end-to-end value chain. Rather than seeing procurement, for example, as a single line of business, take into account the entire value chain within procurement. This process helps identify all of the players and what their value propositions are, “And will also identify where the value leaks are in the whole chain,” he said. The solutions created to fix those leaks are potential revenue-generating products.

    Analytics Best Practices

    Southekal published a book, Analytics Best Practices. He said that the book offers prescriptive and practical guidance that can be used in a variety of settings. His goal was to address the four pillars of analytics — Data Management, Data Engineering, Data Science, and Data Visualization — with ten best practices, and to do so by focusing on concepts rather than on specific tools or platforms. “It’s practical, it’s complete, it’s neutral.”

    DBP-Institute

    DBP Institute helps companies get the most out of their digital technologies and data by implementing new solutions or by optimizing existing solutions, he said. They work primarily in higher education and corporate settings, as well as offering analytics education and training online and at conferences.

    Recipes for Success

    Because of the effects of the COVID-19 pandemic, many companies are turning to data and digital technologies as key enablers. Southekal created a reference architecture document he calls The Reference Architecture for Digital Enablement (TRADE) to help companies with their digital enablement and analytics initiatives. Analytics is ultimately about data, he said, but to capture data, you need mechanisms for data storage, processing, and integration. “I’ve collected ‘recipes’ for best practices, and now when I work with customers, I bring TRADE, my implementation cookbook.”

    Author: Amber Lee Dennis

    Source: Dataversity

  • Implementing practices to keep up with developments towards data-driven and consumer-centric

    Implementing practices to keep up with developments towards data-driven and consumer-centric

    Corporate leaders tell us that the processes that, for decades, have framed the internal dealings of their firms are no longer tolerable, as they need to become customer-obsessed. The increasing occurrence of titles such as chief customer officer, chief data officer, and chief digital officer is a tacit admission that firms need a higher level of cross-business-unit coordination to provide compelling customer experiences. Meanwhile, rising customer and employee needs, shorter decision-making cycles, faster technology change and innovation, and the need to avoid margin pressure and commoditization force companies to rethink their entire underlying approach to process.

    Establish New Practices To Drive Speed And Innovation

    Many tech execs and their teams have become quite comfortable with agile practices. Adoption remains strong, and teams are getting better at knowing when and where to use agile, DevOps, SecOps, and continuous delivery. But when expanding agile practices to the enterprise level, tech execs are confronted by a multitude of issues. As one tech exec told us, “We really see the benefits of using agile to drive better customer experiences. But many of our business partners still focus on driving internal efficiencies, which kind of works against our efforts to focus on external client benefits.”

    To overcome these internal roadblocks, tech execs will have to move beyond agile and establish new practices that enable cross-functional teams to drive speed and innovation at scale. These practices will:

    • Embrace ecosystems, individuals, and interactions over linear processes. Future fit leaders will act as “servant leaders” to their organizations, embrace a culture of openness and diversity, accelerate human-machine interactions, and adopt new practices that drive greater flexibility, cooperation, and creativity.
    • Measure customer value instead of internal efficiency. Future fit tech executives will work with their executive peers to define and agree on joint success metrics that cut across different business functions and drive value for customers.
    • Leverage common product and program management principles instead of rigid control. Future fit leaders will create a new set of practices beyond agile software delivery that meld together customer journey mapping, lean portfolio management, and integrated value stream management.
    • Continuously respond to change instead of working the plan. Future fit leaders leverage collaborative, continuous planning, using value streams to identify which platforms, products, and services to prioritize.

    Shifting beyond continuous improvement to continuous innovation isn’t easy and requires strong executive commitment and buy-in. If you are a tech executive, make sure you are aware of the steps you can take to drive faster innovation and sustainable growth.

    Author: Pascal Matzke

    Source: Forrester

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