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Data Analytics Outsourcing Market Analysis, Market Size, Application Analysis, Regional Outlook, Competitive Strategies and Forecasts, 2016 To 2024

  1. Published: January, 2017
  2. Format: Electronic (PDF)
  3. Number of pages: 92
  4. Industry: Communication Services

The worldwide data analytics outsourcing market is poised to rise at a whopping CAGR exceeding 30% from 2016 to 2024 (forecast period). It can report revenues more than USD 6 billion by 2024. Growing awareness about the advantages of data analytics is a key market driver. Businesses are gradually realizing its importance in maximizing revenues and in identifying consumer choices. Not every organization is equipped with the required knowledge and resources for effective data analysis. Furthermore, the scarcity of professionals for data analytics hampers the development of competitive & advantageous data analysis. This in turn has further fueled the demand for services in effective data analysis. A number of organizations have begun outsourcing the same.

Organizations utilize data analytics tools for better decision making, improved customer services, low operating costs, and greater efficiency. Companies use data analytics for examining huge amounts of data via quantitative & qualitative techniques. Nowadays, organizations produce amounts of data because of widespread adoption of mobile devices, popularity of social media, and the availability of comprehensive multimedia content. At present, the global data analytics outsourcing market is in the nascent stage; however, it is expected to register a high growth rate in the forecast period. Data security issues may negatively impact market demand during this time. Surging investments in social analytics and real time analytics may help mitigate deficiencies in demand. Such investments may create lucrative growth opportunities for companies operating in this industry.

The four key parameters based on which the data analytics outsourcing market is fragmented are: types, verticals, applications, and regions. Based on types, the industry is trifurcated into predictive, prescriptive, and descriptive. Owing to ease of use and extensive application, descriptive analytics holds a major share in the overall market. This segment can generate the highest revenues during the forecast period. Prescriptive analytics too, are expected to expand robustly because of widespread adoption of the same across a host of applications. Prescriptive analytics helps optimize decision making by showing companies the actions that they could take for profit maximization amidst various business constraints.

Descriptive analytics is a stage in data processing that systematically develops an outline of historical data for yielding useful information. It includes data mining and data aggregation. Data visualization, reporting, and querying are the other tools for gathering more insights. Predictive analytics, a part of advanced analytics aims at making predictions about future events. It utilizes tools, such as artificial intelligence, modeling, data mining, machine learning, and statistics for the analysis of current data.

The verticals across which this market is segmented are media & entertainment, BFSI (banking, financial services & insurance), telecom, retail, and healthcare. Data analytics has been a major game changer over the past couple of years. Industrial application of the same helps reveal hidden patterns, identify customer preferences & market trends, and present unknown correlations. All these together may lead to enhanced operational efficiencies, newer revenue opportunities, competitive advantages, and effective marketing.

Data analytics in BFSI aims at tackling issues, such as illegal trading activities, card frauds, and money laundering among others. It is widely used by hedge funds, major banks, and retail traders. Data analytics also finds use in areas, such as environmental protection, energy exploration, and health-related research activities. On the basis of applications, the divisions are risk & finance analytics, sales analytics, marketing analytics, and supply chain analytics.

Regions in the global data analytics outsourcing market are Europe, North America, Asia Pacific, Latin America, and the Middle East & Africa. The industry is led by Asia Pacific. India, Philippines, and China are the three main providers of this region. Factors propelling the Asia Pacific market are low labor costs, skilled labor force, well developed telecom infrastructure, and strong technology infrastructure. High demand in the region is because of globalized processes, improved operations, and the widespread implementation of information technology.

There is however a number of challenges facing this regional market, such as greater preference for local outsourcing and the misleading idea among consumers that low-cost nations offer inferior quality services. Data analytics outsourcing faces huge demand from Western Europe and North America. Middle East Asia, Eastern Europe, and Latin America may expand robustly over the forecast period.

Prominent competitors in the worldwide data analytics outsourcing market are Genpact Ltd., ZS Associates Inc., Wipro Ltd., Tata Consultancy Services Ltd., Accenture, and Capgemini. Most outsourcing service companies aim at collaborating with leading firms in order to expand their consumer base. Capgemini boasts of a global Data Science & Analytics approach that binds the newest developments in data science methods with leading skills in business consulting. This is done for creating solutions & models that match various information needs.

The company conceptualizes models of real life problems to foster effective decision making. These models offer insight for the quantification of risks and also provide the benefits related with solutions to intricate business problems. With the aid of big data technology, it provides solutions to almost each and every facet of business operations. The company offers solutions for social media analytics, Internet of Things analytics, customer insight, CFO analytics, business process analytics, and advanced planning & scheduling.

Under predictive analysis, there are two main methods that are widely used; namely ARIMA (autoregressive integrated moving average) and ANN (artificial neural networks). In the ANN method, data is processed in the same way as in biological neurons. Data moves into the mathematical neuron, gets processed, and then the result is generated. This process acts like a mathematical formula that gets repeated several times. Neural networks in the human brain have the capacity to connect neuron sets in layers. This leads to the creation of multidimensional networks. Output from the first layer acts as the input for the second layer. This situation keeps repeating itself with each layer. This entire process helps identify regularities & certain associations present in set patterns.

The ARIMA model is used in time series analysis. Time series analysis uses past data to model existing data in order to make future predictions. The model inspects autocorrelations, by comparing how there’s interdependence between past & current data values. This is especially important when it comes to deciding the number of steps into the past to be considered while making predictions. Each aspect of ARIMA tackles a different area of model creation. The AR (autoregressive) part aims at estimating the current value by taking into account the previous one. Differences between real values and predicted data are managed by the MA (moving averages) part. 

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