SPAN White Paper
Data Analytics Turning information into insights In today’s business scenario, Data is defining a whole lot of organizational operations; it is not only a tool to assist a business strategy, but also helps achieve a new competitive advantage. Human decisions, plans and strategies have always been dependent on refined and contextual data i.e. information. Data Analytics is a great approach to tackle, analyze, manage and interpret widely diverse and incredible volumes of data. In other words, it can simply be referred to as smart information management. Thus, capturing genuine opportunities can be productively realized across multiple industries through near real-time data analysis and actionable insights. This white paper investigates the requirements, nature of data types, work processes, challenges, advantages and solutions across the entire data analytics segment.
Life Cycle of an Analytics Project
Identify the top influencers from the data
Understanding the business need / vision Relevance, Readiness & Preparedness of existing data
Test the model for accuracy and tune it Derive and Evaluate the right Analytics Model
Productize the Analytics Model
Fundamentals to Data Analytics Identifying the Data
Insight
It is important for a data analytics project to identify where the valuable information resides and map it based on the 3V framework, defined by Volume, Variety and Velocity.
The success of a data analytics project depends on the quantifiable insight it generates. The derived system should be able to provide timely and accurate answer to the business questions. This presents a valuable actionable insight that marks a way to tread and adds up to the value chain.
Data Quality Data quality is the core aspect in data analytics that decides its intended use in business operations and decision making. The correctness and consistency of the data demonstrates its quality and fitment for use. Business Objectives Clarity in defining your goals and objectives are essential for achieving success through analytics. Analysts need to have in mind the big-picture while building the conceptual framework and process useful in data analytics. Data Availability & Access Data availability and access is the fundamental requirement to data analytics. Authorized personnel should be able to access the internal organizational data, and, the information external to the organization has to be collected from reliable resources.
Data Visualization For a meaningful insight, it is a must to present the information in an appealing and insightful manner to the intended audiences. The business story and the user story should be represented with advanced visualization techniques for better clarity, with scope for interactive exploration. Data Practices The right data analysis framework, a standard architecture for data interoperability, and strict compliance to data security & privacy norms creates a trusted environment for data analysis. These enablers help organizations to undertake projects that assure high data security for a project’s success and stakeholders’ buy-in.
A data analytics project fails if it lacks the right resource or is considered as a mere IT initiative. The Big-Bang approach, i.e., to analyze the impacts of the data model right from the start proves constructive. Data Analytics | 2
Data Sources Review
Social Media
External Data
News & Journals Internet of things
Mobile
Data Source
User Generated Data
Structured Data
Traditional Enterprise Data
Edge of the Enterprise
Data from Partners
Cloud
Data Aggregators
While the structured data from various legacy systems still takes the largest chunk of the data block in any data-driven environment, a huge amount of rapidly expanding unstructured data from various external sources and devices is becoming critical for a successful data analytics project. Today, businesses have greater potential to analyze customer preferences by identifying trends from customer interactions through business touch-points. The mobile outreach across the world is reported to reach 7.3 billion by the end of 2014 owing to the enormous number of active mobile phones. Mobile devices, Internet of Things (IoT), data from partners, cloud, and data aggregators, are the vital components that can independently contribute towards redefining the architecture of data analytics. External to the enterprise is the information such as user generated data, reviews, news & journals, and social media that can be extracted smartly to generate great possibilities to help businesses build better products and services through real-time access to information. The widening gap between the enormous amount of data generated through customer interaction, web logs, purchase history and customer behavior and strategy to gain actionable insights is a matter of concern for many organizations.
The Structured Data, Enterprise Data and External Data provide a comprehensive insight and collective understanding to kick start the data analytics process. Comparing conventional and new methods, the data analytics trends can be explored to changeover from descriptive operations to predictive actions an upward traverse from information to insights.
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Phases in Data Analytics
Mission
Preparation
Modelling
Actionable Insights
Enactment
Addressing the gaps & issues between each stage helps overcome the challenges related to Stakeholder Buy-In, Data Readiness & Quality, Data Security & Privacy, and Inadequate Technical Infrastructure. Challenges in Data Analytics Readiness Firm readiness of an organization makes it possible to overcome diverse challenges. Business Sponsorship Executive sponsorship / stakeholder buy-in is the topmost challenge in introducing and implementing data analytics. As a strategic initiative, an organization needs to educate its stakeholders regarding the importance of using the bottom-up approach to implement the data analysis concepts so that the advantages are well understood. Integrated data analytics aids business improvement and aligns the whole organization with a data-driven culture. Data Availability & Relevance Availability of the data relevant to the organization and its expertise is another key factor. Utilizing Data Aggregators – organizations that compile facts and figures from detailed databases with information on individuals, and selling the same to other concerned companies, is a significant move. Further, providing access to Trail Users in order to determine their characteristics such as, where they come from, their objectives & perceptions, demographics and activity patterns, serve as a powerful tool. The final step is to collect information and arrange it in an orderly manner, so as to gain an insight and draw pertinent derivations.
It is essential to overcome challenges such as achieving executive sponsorship, data availability & relevance, and where to start the project from. Resources To avail real-time information and business value from data, it is substantial to have all the corresponding resources. Availability of Statisticians, Data Specialists & Domain Experts Bringing out meaning from the data is a combination of industry expertise and knowledgederived outputs from data specialists, statisticians and experts. With the technology evolving much faster than the dexterity of the workforce, availability of multi-skilled in-house data scientists to make sense of the statistical data is crucial. It is especially useful for those sectors focusing on big data analytics and adopting novel industry trends. Data Analytics | 4
Project Team and Process Incorporating the Agile method in projects makes tasks more flexible, and helps achieve constant progress. At regular interims, it is important to revisit and realign the project’s goals. The processes involved in the course of executing a project are simplified by tools. Plus, the changes at the front-end can be easily implemented as the back-end is already well-programmed algorithmically.
Data scientists + Agile method = Ensures success in data analytics. Technology Investment Database As the data management space progresses, technological advancements have launched revolutionary tools that could be ground-breaking solutions to adopt and execute data analysis. Technology helps to extract information from compressed data, even in memory. The only challenge is – Can your organization utilize and explore the benefits of the technology in its data analytics strategy? Hadoop Unlike proprietary and expensive systems, the Hadoop framework enables parallel and uniform processing of massive data amounts through industry-standard servers. With Hadoop, there is no requirement to know how to query the data prior to storing it, as Hadoop allows you to decide it later. Hadoop provides breakthrough advantages that are pivotal support solutions for businesses to find accurate value in the data, which could have otherwise been discarded. Processors and Cloud Maintaining a well-designed auto-functional columnar database, high processing machines and, incorporating a cloud infrastructure for smart data storage are the challenges that organizations require to encounter. testing your data model on two or more technologies can reveal the accuracy and correctness of the model, which provides options for improving as well as improvising your data models.
Investing in technologies and using them for a greater advantage in analytics reduces the programming effort & augments productivity. Data Governance Data governance is an indispensable component and measure to strengthen data integrity. Correctness, completeness and compliance are the centers of focus when it comes to data privacy and security. The challenge lies in ensuring whether all the parameters are in place. Data Privacy & Security Protection Data governance includes protecting the data against unauthorized access. Disseminated and processed information resources require dynamic masking and industry-specific compliance. This can be achieved by averting deliberate or accidental insertion, destruction or modification of data within a database.
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Implementation Once a data model is out, it is essential to identify its authenticity and value, and trust the model’s efficacy. Developing such a model is a phenomenal challenge in itself. Data Model Validation “ART” enables it to work; science proves it works”. With the Assessment of Risks and Tasks (ART) tool, a data model can be exceptionally validated. Moreover, business users must be allowed to test the utility aspect and analytical capability of the model. It is also vital to state the assumptions and goals of the project, and test the fitment factor of the model.
Validate & re-validate the outputs of the analytics model via business users. Conclusion Data analytics concepts offer a strong foundation to execute solutions that can increase an organization’s efficiency, improve its operations, boost sales, and enhance customer relations and support. These solutions help businesses deploy data analytics more quickly, with validated configurations. Organizations can augment their proficiency by concentrating on areas such as Data Clustering, by grouping similar data points to provide new understanding of familiar situations. SPAN presents huge opportunities to businesses that aim on building upon their analytics architecture. With its expertise in Sentiment Analysis, SPAN analyzes the opinions of users on social media platforms, and adeptly identifies the ‘Web Footprint’ of users by tracking their online visibility and activity. This helps organizations improve their capabilities and enrich their existing systems to aid information streaming and obtain real-time results.
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About SPAN: SPAN is an established Software Services Company offering comprehensive IT services since 1994. Our clients include Fortune 1000 companies, Independent Software Vendors and start-ups. SPAN’s Offshore Development Center in India is CMMI Level 5, PCMM Level 3, ISO 9001:2008 and ISO 27001:2005 certified. SPAN has a global footprint with offices in the U.S., India and group offices in Europe. There are multiple offshore development centers in Bangalore and Chandigarh, India. SPAN is ranked #7 Best IT Employers in India by a leading IT publication. SPAN’s Relationship Management (RM) Model is a well-defined, yet flexible framework, which provides ongoing business value to both, the client and SPAN. SPAN is wholly owned by USD 2.3 Billion Norwegian IT services major EVRY (www.evry.com).
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