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The BADIR (pronounced /ˈbaːdɪr/) [1] is a structured data science and data analytics process designed to enhance data-driven decision-making within organizations by addressing both analytical output as well as usefulness to management. It was developed by [2] Piyanka Jain and Puneet Sharma and first published in the 2014 book “Behind Every Good Decision”. [1]
The BADIR Framework employs a hypothesis-driven approach that involves understanding business objectives and challenges, analysis planning before acquiring and ensuring the quality of relevant data, applying analytics to derive insights and the potential impact on the business challenge, developing actionable recommendations aligned with strategic goals, and implementing decisions while monitoring outcomes and making necessary adjustments. [3]
The BADIR Framework consists of interrelated components essential for decision-making. [4] Its main assertion is that if data analytics does not drive business impact, then it is just statistics, not analytics. The acronym in the framework stands for the following 5 steps: [5]
Piyanka Jain, a data science expert with experience at Adobe and PayPal, developed the BADIR framework to address the need for more structured data analytics methodologies. Observing that data-driven decision-making was often seen as complex and inaccessible, Jain designed BADIR as a five-step process to streamline and simplify data analysis in business environments. [2]
[6] According to Jain, the framework's focus on defining the business question early is said to optimize up to 80% of a business leader's workflow by reducing guesswork and concentrating efforts on strategic goals. [7] IMA's Strategic Finance magazine compared the framework to the scientific process in terms of its approach to data analysis, and noted its ability to improve cross-departmental efficiency. The framework has been recommended for a variety of business leaders, from customer service managers to records and information management professionals.
Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.
Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data science. Analytics also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.
Data management comprises all disciplines related to handling data as a valuable resource, it is the practice of managing an organization's data so it can be analyzed for decision making.
The Five Ws is a checklist used in journalism to ensure that the "lead" or "lede" contains all the essential points of a story. As far back as 1913, reporters were taught that the lead/lede should answer these questions:
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Business analysis is a professional discipline focused on identifying business needs and determining solutions to business problems. Solutions may include a software-systems development component, process improvements, or organizational changes, and may involve extensive analysis, strategic planning and policy development. A person dedicated to carrying out these tasks within an organization is called a business analyst or BA.
Technology strategy is the overall plan which consists of objectives, principles and tactics relating to use of technologies within a particular organization. Such strategies primarily focus on the technologies themselves and in some cases the people who directly manage those technologies. The strategy can be implied from the organization's behaviors towards technology decisions, and may be written down in a document. The strategy includes the formal vision that guides the acquisition, allocation, and management of IT resources so it can help fulfill the organizational objectives.
Data and information visualization is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. Typically based on data and information collected from a certain domain of expertise, these visualizations are intended for a broader audience to help them visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data. When intended for the general public to convey a concise version of known, specific information in a clear and engaging manner, it is typically called information graphics.
Intelligence analysis is the application of individual and collective cognitive methods to weigh data and test hypotheses within a secret socio-cultural context. The descriptions are drawn from what may only be available in the form of deliberately deceptive information; the analyst must correlate the similarities among deceptions and extract a common truth. Although its practice is found in its purest form inside national intelligence agencies, its methods are also applicable in fields such as business intelligence or competitive intelligence.
Operational intelligence (OI) is a category of real-time dynamic, business analytics that delivers visibility and insight into data, streaming events and business operations. OI solutions run queries against streaming data feeds and event data to deliver analytic results as operational instructions. OI provides organizations the ability to make decisions and immediately act on these analytic insights, through manual or automated actions.
Technology Intelligence (TI) is an activity that enables companies to identify the technological opportunities and threats that could affect the future growth and survival of their business. It aims to capture and disseminate the technological information needed for strategic planning and decision making. As technology life cycles shorten and business become more globalized having effective TI capabilities is becoming increasingly important.
Artificial intelligence marketing (AIM) is a form of marketing that uses artificial intelligence concepts and models such as machine learning, Natural process Languages, and Bayesian Networks to achieve marketing goals. The main difference between AIM and traditional forms of marketing resides in the reasoning, which is performed by a computer algorithm rather than a human.
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Decision intelligence is an engineering discipline that augments data science with theory from social science, decision theory, and managerial science. Its application provides a framework for best practices in organizational decision-making and processes for applying computational technologies such as machine learning, natural language processing, reasoning, and semantics at scale. The basic idea is that decisions are based on our understanding of how actions lead to outcomes. Decision intelligence is a discipline for analyzing this chain of cause and effect, and decision modeling is a visual language for representing these chains.
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Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data.
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