How Organizations Leverage Data Science Tarun Sukhani NetCom Learning
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AGENDA Data Science in Organizations Trends in Data growth Key Challenges 5 Strategies You can apply in Your organization
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DATA SCIENCE IN ORGANIZATIONS
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THE ORGANIZATIONAL PROCESS
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DATA SCIENCE METHODOLOGY IN ORGANIZATIONS
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WHY IS DATA SCIENCE IMPORTANT TO ORGANIZATIONS? Data can help you communicate efficiently and conveniently with your customers. For example, it can help to better serve a customer to maintain detailed account and purchase history.
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#2 WHY DATA SCIENCE IS IMPORTANT IN ORGANIZATIONS? It is possible to re-develop or introduce new products through data collection and using data. You can take this information and implement the learning of future designs if you collect data in a way that tells you how your product is being used and perceived by your market.
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#3 WHY DATA SCIENCE IMPORTANT IN ORGANIZATION? Operational efficiency can be transformed with big data. Data Analytics can help to improve manufacturing, distribution, inventory management, staffing, and many other areas. According to McKinsey Global Institute, widespread adoption of Big Data Analytics could increase GDP of manufacturers and retailers by $325b, by 2020.
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DATA SCIENTIST CAN ADD VALUE TO ANY BUSINESS
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TRENDS IN DATA GROWTH
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TRENDS IN DATA GROWTH In the prediction and preparation of a global crisis and its aftermath, Big data and analytics combined with artificial intelligence (AI) technologies all seem to be paramount.
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#1 SMARTER, FASTER, MORE RESPONSIBLE AI By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures. Within the current pandemic context, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) are providing vital insights and predictions.
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#2 DECLINE OF THE DASHBOARD Visual, point & click authoring and exploring will be replaced by dynamic data stories with more automated and used experiences. The time that users use predefined dashboards therefore decreases.
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#3 DECISIONS INTELLIGENCE Decision intelligence unites a number of disciplines, including decision management and support for decisions. It provides a framework for data and analytical leaders in the development, composition, modelling, alignment, execution, monitoring and definition of decision models and processes in the context of their business results and behaviour.
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#4 X-ANALYTICS Due to thousands of research papers, news sources, social media posts and clinical trial information, AI was critical during this pandemic to help medical and public health experts predict the propagation of diseases, plan capacity, identify new treatments and vulnerability of populations. In the identifying, forecasting and planning for natural and other business crises and opportunities in the future, X analytics together with AI and other technology such as graphical analytics (another top trend).
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#5 AUGMENTED ANALYTICS Augmented analytics automates the process of gaining business insights through advanced artificial intelligence and machine learning. An enlarged analytics engine automatically transmits, cleans and analyses the data of an organisation. As a final step, it turns the insights of a tech person into workable steps with little supervision. Increased analytics can make analytics more user-friendly for smaller businesses.
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#6 CLOUDS Data and analytics leaders need to prioritise workloads to leverage cloud capacity and focus on cost optimization and other advantages such as shifting to cloud and accelerating innovation.
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#7 DATA AND ANALYTICS WORLDS COLLIDE The data and analysis tools and capability into the analysis stack are incorporated in order to converge the collision into a constructive convergence. Beyond tools to promote communication and collaboration, focus on people and processes. Use the data and analysis ecosystems to provide coherent stacks through an enhanced approach.
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#8 DATA MARKETPLACE AND EXCHANGES In order to consolidate data offers third parties, the data markets and exchanges provide a single platform. These markets and exchanges provide a centralised access (for example, to X analytics and other unique data sets) to save scale to reduce costs for data collected by third parties.
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#9 BLOCKCHAIN IN DATA AND ANALYTICS Blockchain technologies tackle two data and analytical challenges. First, blockchain gives the entire asset and transaction lineage. Second, for complex networks of participants, blockchain provides transparency. Data and analysis should complement blockchain technologies by highlighting the inadequacies between information management infrastructure and blockchain technologies, which supplement their existing data management infrastructure.
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KEY CHALLENGES
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DATA SCIENCE CHALLENGE IN ORGANIZATION
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#1 THE AMOUNT OF DATA BEING COLLECTED A data system that collects and organises information automatically is required. This is far too timely and unnecessary in today's environment to perform manually. An automated system allows employees to take advantage of the time spent processing data.
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#2 LACK OF SUPPORT Without organisational support, both top and lower employees can't be effective data analytics. In many ways, risk managers will be powerless if management does not empower them. Other staff also have an important role to play: unless they provide analytical data or the risk manager has no access to their systems, it is difficult to create any information which work.
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#3 TROUBLE FINDING AND KEEPING QUALIFIED DATA SCIENTISTS Another stratum of problems is the retention of talent, which is closely linked to the frequency with which experts in data science demonstrate the value of their work. 44% of data scientists across the committee said that they intend to find another job in the coming year.
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#4 ELIMINATING BIAS AND EXPLAINING MACHINE LEARNING "We find slow progress in addressing bias among all the trends identified in our study and fairness and making machine learning the most important things to explain," said the report. The problems surrounding machine learning and artificial intelligence were all problems, responsibility and equality. Anaconda stated that companies "should treat ethics, explainability, and fairness as strategic risk vectors and treat them with commensurate attention and care."
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#5 VISUAL REPRESENTATION OF DATA Data must often be visually presented in graphs or charts in order to be understanded and impactful. Although these tools are incredibly useful, manual construction is difficult. It is frustrating and timeconsuming to take the time to collect information from various areas and put it in a reporting tool.
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5 STRATEGIES YOU CAN APPLY IN YOUR ORGANIZATION
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BIG DATA STRATEGIES For the organisations, the Big Data strategy should be operable. To this end, organisations can take the following five-stage approach to formulating their Big Data strategy: • Define business objectives • Execute a current state assessment • Identify and prioritize Use Cases • Formulate a Big Data Roadmap • Embed through Change Management
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BIG DATA STRATEGIES
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#1 DEFINE BUSINESS OBJECTIVES The Big Data strategy should align itself with corporate goals and address key business problems, as the main objective of big data is the collection of value through the use of data. One way to achieve this, since most organisations are already in place, is in line with the company's strategic planning process. The following figure provides examples of frequently occurring business goals from a recent survey:
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… #1 DEFINE BUSINESS OBJECTIVES
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#2 EXECUTE A CURRENT STATE ASSESSMENT This step focuses mainly on the assessment of current business processes, sources of data, data assets, technological assets, capabilities and policies or the company. This exercise aims to help analyse the current state and the desired future state.
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#3 IDENTITY AND PRIORITIZE USES CASES
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#4 FORMULATE BIG DATA ROADMAP The Roadmap is designed to identify gaps in the data architecture, technology and tools, processes and of course people with regard to the desired future (skills, training , etc.). A multitude of strategic options for initiatives will be outlined in the current assessment and use of cases. The next objective will be to make the options priority based on complexity, budget and potential benefits.
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...#4 FORMULATE BIG DATA ROADMAP
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#5 EMBED THROUGH CHANGE MANAGEMENT Change management should involve corporate change, cultural change, technological change and business process change. Data management is an essential component of change management, addressing the overall management of data availability, usability, integrity and data security. Suitable incentives and ongoing measures should be a key part of any programme for managing change.
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...#5 EMBED THROUGH CHANGE MANAGEMENT
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