Data and Analytics, today, play a key role in competing with rivals. Every passing day leads to creation of enormous amounts of data by organizations across the globe. These huge data lakes often go unused, or are underutilized, by organizations. This data, if utilized properly, is of great assistance in informed decision making.
Multiple data types and sources generated by discrete systems are often inconsistent, dispersed, and lacking integration, which makes them unworkable. Such data results in inaccurate analysis and flawed insights. Reliability and confidentiality of data can be ensured by stipulating rules and processes to govern access to data and its Metadata.
Metadata can be defined as “the Data in the context of Who, What, Where, Why, When, and How.” It’s the information pertaining to the data itself, its attributes, and elements. Metadata provides searchable key attributes of information to the users e.g., Customer ID or Name. Appropriate identification of Metadata is a major step in uncovering the potential locked in enterprise data assets.
Metadata Management relates to handling of data, its description, relationships, and lineage within an organization. Metadata enables a user to search and identify information on certain key attributes. Context of data is of prime importance in managing Metadata.
Metadata isn’t all about identification of data. With ever-increasing volumes and complexity of data, Metadata management is getting critical to identify informational assets and convert those into enterprise assets of high business value. This entails setting up policies and ensuring efficient information management. Metadata Management integrates all data at the enterprise level.
Benefits of Metadata Management
- An efficient Metadata Management system helps the business users to comprehend the source of the data characteristic and the calculated measure of that characteristic.
- It supports the technical users in mapping business Metadata with technical Metadata.
- Metadata Management provides a holistic view of the various data systems in an organization.
- It enables automated parsing and loading of variety of Metadata types.
- Building an Enterprise Metadata model based on the data generated from discrete systems—e.g. data warehouse, integration tools, and data modeling tools—is quite efficiently done through Metadata Management.
- Mitigation of any challenges in data accessibility and utility.
- Enhancement of data quality.
- Supporting Digital Transformation by creating data reporting and data analysis experts.
People in the same organization perceive Metadata differently. Difference of opinion in the identification of Metadata within the company results in inadequate visibility and access to data. This is where a broader classification of the types of Metadata is helpful. A thorough understanding of the different classes or categories of Metadata assists in developing a standardized perception of data across the organization. These categories include:
Structured Metadata provides information on what the data looks like, e.g., data elements names mapped to columns, descriptions of data elements, data types, length of data elements, and the file layout. This can include tags, primary keys, or foreign keys.
Entails information associated with data origination point, directives, constraints, owners, service level agreements for consumption of data, demographic information about the data asset e.g., size, number of records, date of production, or source of origin of data.
Refers to data production processes, including data lineage, any 3rd-party sources of data, derivations of data elements, or the process flows related to data pipelines.
Describes information on the context and classification of data. It includes a glossary of business terms, definitions, taxonomies, master data, historical data, types of queries performed etc.
Provides data on Metadata consumers, their roles, data owners, and data stewards responsible for managing the quality and usability of data.
Interested in learning more about the other categories and classifications of Metadata? You can download an editable PowerPoint on Metadata Management here on the Flevy documents marketplace.
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Business dashboards are important tools to measure key performance indicators and data pertaining to an organization or certain procedure. Just as a vehicle dashboard is powerful performance management tool in summarizing a performance of a multitude of processes, a business dashboard summarizes the performance or impact of a host of functions, teams, and activities; and assists in strategic planning and decision making.
Business dashboards simplify sharing and analysis of large data, and help users visualize complex performance data in simple yet visually aesthetic manner. Dashboards aid in simplifying complex processes into smaller more manageable information pieces for the organizational leadership to focus on everyday operations. They keep everyone on the same wavelength and prioritize display of facts based on their importance and potential impact. The information on a well-designed dashboard is clear, presentable to enhance meaning, readily accessible, and dynamic. A carefully-planned dashboard allows the leadership to identify and answer business challenges in real-time, develop plan of action based on insights, and inculcate innovation.
Proficient and capable dashboard designers and firms have taken the art of visualization of valuable indicators and insights through dashboards to the next level. They have devised specific guiding principles, dos and don’ts, and time-tested development routines to accomplish this. These guiding principles comprise 10 best practices, which can be segregated into 3 major implementation categories:
- Analyze your audience
- Contemplate display options
- Prompt application loading time
- Exploit eye-scanning patterns
- Restrict number of views & colors
- Let viewers filter data
- Ensure proper formatting
- Use Tooltips to reinforce story
- Eliminate redundancy
- Review the dashboard carefully
Let’s discuss the first 5 best practices for now.
Analyze your audience
A careful analysis and understanding of the business dashboard’s intended audience is the first important principle to consider before commencing the development of such a dashboard. For instance, a busy salesperson in need of quickly going through indicators, whereas senior management needing a deep-down review of quarterly sales results. This gives the developers a thorough idea of what the audience wants from a dashboard, what data they will visualize utilizing this, and let them know the audience’s technical capabilities in terms of data analysis, theme, issue, and business understanding.
Contemplate display options
The second principle to follow in designing a business dashboard is to research your users’ device and display preferences beforehand. Building a dashboard with desktop display options in mind when your audience prefers to use phones to view it could be a disaster. The designers should set the size of the dashboard properly—allowing the users to view it on a range of devices, by building in automatic sizing option for the dashboard to adopt to the dimensions of the browser window.
Prompt application loading time
Your audience and viewers are busy people who hate long waits. Therefore a stunningly designed dashboard would not get the right traction if it takes too much time to load. The dashboard author should facilitate prompt dashboard loading by deciding which filters to add in the dashboard and which ones to exclude. For instance, although filtering is useful in restricting the amount of data analyzed, it effects query performance. Some filters are quite slower than others as they load all of the data for a dimension instead of just what you want to keep. Knowing the Order of Operations is also beneficial in reducing the load times.
Exploit eye-scanning patterns
The dashboard authors should have a deep sense of the main purpose of the dashboard in mind when develop such a tool. They need to be aware of individuals’ eye tracking patterns—typically when most people look at a screen or content, they start scanning the upper left hand corner of the screen first by intuition—and make the best use of the screen space to display the most important content at the right place.
Restrict number of views & colors
The designers often get over enthusiastic during their application designs and try to stuff the dashboard with multiple relevant views. This is detrimental for the bigger picture. They must include not more than 2 to 3 views per dashboard and create more dashboards in case the scope creeps beyond the 2-3 views range. It is also crucial to ensure the content to be clearly visible to the viewer and to use colors correctly to facilitate analysis instead of cramming too many colors in the visuals, which creates a graphical overload for the viewers, slacken analysis (or may even prevent users to analyze data), and even blur the graphics.
Let viewers filter data
Allowing users to filter the data is another best practice to keep in mind while designing business dashboards. This added interactivity encourages data assessment and permits the users to have their most important view act as a filter for the other views in the dashboard. This helps in conducting side-by-side analysis, promotes involvement, and retains users’ interest.
Interested in learning more about the other best practices to aid in designing a robust business dashboard and knowing the most common mistakes to avoid in this process? You can download an editable PowerPoint on Business Dashboard Design here on the Flevy documents marketplace.
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Today’s customers are better informed, better connected, and more demanding than ever before. CEOs are now concerned about Customer Loyalty and they recognize that mastery of the customer agenda is essential. In fact, global leaders of successful businesses recognize that creating a customer-centric, digitally-transformed business is a top priority.
In this age of digital disruption, how can organizations engage customers, increase Customer Loyalty, and achieve profitable growth? What is most appropriate when it comes to Customer-centric design?
Almost every market is experiencing a fundamental change. Consumer expectations have shifted and digital technologies are making the biggest impact on businesses large and small since the start of the information age. Ultimately, businesses need to navigate the challenges of digital disruption and find new ways to create economic value and drive growth.
The challenge today is what it takes for organizations to be a Customer-centric Organization.
Unraveling the 6 Core Capabilities of a Customer-centric Organization
A Customer-centric Organization must have 6 Core Capabilities to compete in the Digital Age. In this global time, customer-centricity ceases to be a differentiator. It has become a matter of survival.
The first 2 Core Capabilities are Customer-directed. These are Customer Strategy and Customer Experience (CX).
- Customer Strategy. The first core capability, Customer Strategy is focused on addressing changing customer needs and behavior. It involves the development of a clear view of customer behavior and intentions using data and analytics. Customer Strategy can be applied in several ways. It can be used to refine and develop a proposition or even inform major investments in new media content.
- Customer Experience (CX). Customer Experience (CX) is that core capability that generates a significant competitive advantage – a double revenue growth against industry counterparts. It is being able to respond to customer needs balanced with understanding the values customers bring to the enterprise. The world’s most advanced customer businesses often undertake customer journey mapping and experience design which are critical to executing customer-centric change.
The second 2 Core Capabilities focus on front office capability and across the enterprise value chain. These are Sales & Service Transformation and Connected Enterprise.
- Sales & Service Transformation. As the third core capability, Sales & Service Transformation is essential to becoming a customer-responsive business. This is a newly digitized and fully integrated front office capability that can attract, engage, acquire, and continually engage with customers. With the modernization and transformation of front office functions, Marketing, Sales, and Service teams get to have better ideas on how to work together more effectively. This leads to a full end-to-end Business Transformation. A core concept to Service Transformation is the development of Service 4.0 capabilities.
- Connected Enterprise. Focused on delivering differentiated Customer Experiences, Connected Enterprise is an architecture of fundamental capabilities that work across the Enterprise Value Chain, from back office operations through customer-facing interactions. The application of Connected Enterprises has led to companies experiencing an increase in annual revenue and a positive return on investment.
The third 2 Core Capabilities are Data & Analytics and Digital Transformation — your company’s response to a highly demanding digital market.
- Data & Analytics. The fourth core capability is Data & Analytics. This core capability is focused on creating actionable insights that drive profitable growth. With the use of Data & Analytics, it can uncover patterns of customer behavior, relevant social media influencers, and channel preferences. It is useful in personalizing propositions, channels, marketing communication, and the experiences offered to customers.
- Digital Transformation. The sixth core capability, this is the core capability that can power new ways to engage customers, optimize operations, and transform products. Digital Transformation is delivering the right customer and digital technology. With the advent of virtual reality, augmented reality headsets, the Internet of Things, AI, and cognitive computing, it has changed the way customer-centric companies engage customers. Digital Transformation is not an overnight event. This is a series of incremental steps, each delivering a concrete business advantage.
Developing the 6 Core Capabilities is no easy task. It can be pretty challenging. Companies need to have a good handle of its key challenges and the right approaches to mastering the 6 Core Capabilities. When this is achieved, the high road to global competitiveness is achieved.
Interested in gaining more understanding of these 6 core Capabilities of a Customer-centric Organization? You can learn more and download an editable PowerPoint about the 6 Core Capabilities of a Customer-centric Organization here on the Flevy documents marketplace. There is a series of 3 presentations – Part I, Part II, and Part III that discusses all 6 Core Capabilities.
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The Data Analytics Revolution is here. It is transforming how companies organize, operate, manage talent, and create value. In fact, advanced data analytics is now a quintessential business matter. It is important for CEOs and top executives to be able to clearly articulate its purpose and translate it into action. Yet, this is not so.
CEOs and top executives are finding it difficult to articulate the clarity of purpose and act on it. It must not just stay in an Analytics department but must be embedded throughout the organization where the insights will be used. Leaders with strong intuition do not just become better equipped to kick the tires on their analytics efforts. Leadership Development now calls for leaders to be capable of addressing many critical top management challenges. It now requires employing a range of tools, employing the right personnel, applying hard metrics, and asking hard questions.
Data Analytics is a means to an end. It is a discriminating tool for identifying and implementing a value-driving answer. It can unleash insights that could be the very core of your organization’s approach to improving performance. This, however, cannot be achieved if there is no clarity in the purpose of your data.
Data Analytics Revolution: Are We Ready?
The Data Analytics Revolution is transforming how companies organize, operate, manage talents, and create value. But are we ready for this? A number of companies are reaping major rewards from Data Analytics. But this is far from the norm. More CEOs and top executives are avoiding getting dragged into the esoteric weeds.
Data Analytics have complex methodologies and there is a sheer scale of data sets. Machine Learning is becoming increasingly more important. For us to be ready in the onset of Data Analytics Revolutions, we need to be capable of addressing many critical and complimentary top management challenges. We need to be able to ground even the highest analytical aspirations in traditional business principles and deploy a range of tools and people.
To be properly equipped on the proper use of Data Analytics, we just need to develop a mindset for Purpose-driven Analytics anchored on 4 guiding principles.
The 4 Guiding Principles of Purpose-driven Analytics
- Ask Clear and Correct Questions. The first principle focuses on generating impact the soonest. Hence, precise questions are asked based on the company’s best-informed priorities. Here, clarity is essential.
- Identify Small Changes for Big Impact. The second principle focuses on generating gains even on small improvements. There is a need to identify small points of difference to amplify and exploit because the smallest edge can make the biggest difference.
- Leverage Soft Data. The third principle focuses on getting quality insights and generating sharper conclusions. It is at this point wherein the use of softer inputs such as industry forecasts, predictions from product experts, and social media commentary are given more emphasis. Soft data is essential when trying to connect the dots between more exact inputs.
- Connect Separate Data Sets. The fourth principle focuses on capturing the untapped value. This principle emphasizes the need to combine sources of information to make sharper insights. When different data sets are examined, the greater is the probability that problems can easily be fixed.
From Learning to Doing: Connecting the Dots
It is not enough that organizations learn about Purpose-driven Analytics. One also needs to be able to put these into effective use. Companies undergoing Digital Transformation must take a multi-faceted approach to analyze data to minimize overwhelming complexity. There are 4 guiding principles for Purpose-driven Analytics implementation. Using these principles will facilitate the effective use of analytics and transform outputs into action.
Interested in gaining more understanding of Purpose-driven Analytics? You can learn more and download an editable PowerPoint about Purpose-driven Analytics here on the Flevy documents marketplace.
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