The term Big Data
encompasses all of the information and statistics relating to a company's sites, database, social networks, and curated analytics. Analysis of all of this information is an opportunity for a company to improve its knowledge of its clients and prospects as well to optimize costs and innovate.
In order to implement a Big Data project, a company must rethink its operation, adopt appropriate technical solutions, and be ready for any new challenges that result. This article will explain some of the challenges posed by Big Data and explain how companies can use Big Data to improve their business strategy.
(or Smart Data/Analytics) is a term that refers to the digital data circulating on social networks and on all web media. It also includes data collected from a company's customers or prospects via connected networks or CRM tools. Big Data encompasses all sectors: trade, health, transport, communities, sports, and (of course) e-commerce. The exploitation and analysis of Big Data enables businesses and professionals to learn about the habits and expectations of their customers and prospects.
Analysis of Big Data comes with a number of unique challenges that present themselves through each step of analysis. Initial challenges associated with Big Data include data capturing and curation, search, sharing, storage, transfer, visualization, querying, updating, and information privacy. Once the data is analyzed, a business is then challenged to use its results to better meet the needs of its consumer base. This includes improved anticipation of consumer demand, offering innovations tailored to emerging needs of the market, improving customer or user experience, as well as reducing business costs by adjusting production, transportation, or delivery.
Companies have come a long way when it comes to the analysis and use of Big Data, but most Big Data is still not being used. A 2014 study conducted by the EMC and IDC indicated that nearly 70% of corporate data is not analyzed. Further exploration into this phenomenon by Markess
indicated that 82% of professionals think that analytics (and more informed use of Big Data and analytics) could improve operations and processes.
The first steps in structuring a Big Data strategy are to identify the goals
of a Big Data project and to define the results
that a company seeks to achieve.
What are your company's needs? Are you seeking to optimize consumer experience? How do production and overhead costs factor into your strategy?
The second step is to aggregate different data sources
. This is where the company can address obstacles posed by its organization, such as poor communication between the different trades and services or different expectations in different services.
The last step of a Big Data strategy is to set up an analysis
of the data collected. Analysis of this data is either completed by internal resources (such as company data scientists) or external resources and analysts who may provide an unbiased, third-party view.
The tools used by a company to analyze and process data must meet three main rules of Big Data: Volume
, and Variety
. Adherence to these rules will allow a company to process large volumes of data from various sources and to share these results in a short amount of time.
When it comes to choosing the best tool suited for data analysis, there are several techniques that may be helpful. The first technique is to check the scope of analyzed data
. Data can come from an enterprise's CRM, social networks, connected objects, or shelves in stores, as well as sensors. A company's strategy must be adapted to its various sources, whether they be internal or external.
The second technique is to adapt to the company's business sector
. Company needs also depend on its sector and each solution will need to incorporate specific features depending on this area of business.
When analyzing Big Data, companies may find it helpful to host the data internally or to opt for a cloud service
. Cloud services allow for a better flow of information and also provide suitable storage space for the vast amount of information.
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