In the present scenario, organizations face irresistible volumes of data, managerial complexities, quickly changing customer behaviours and amplified competitive pressures in almost all the industries or business domains.
New technologies and business innovations, have further created stress with increasing operational channels and professional platforms, which needs aggression to survive in the existing environment.
With data worldwide being steadily growing at 40 to 45 percent annually, Big data analytics can be a prime game changer in the year 2017.
Big data has initiated a paradigm shift accompanied by an affluence of data and information.
It has left no industry intact as it is continuing to transform the way we think about everything from technology to human resources and sales or marketing are no different.
Market research and advisory firm Ovum, assessed that the big data market will nurture from $1.7 billion in 2016 to $9.4 billion by 2020.
As the market is maturing, innovative encounters are changing, technology requirements are transforming, and the vendor criteria are also adapting. The year 2017 looks to be a demanding one for big data opportunities and prospects.
There are 120 + big data tools which you read more here.
Key diagnoses for big data analytics
Three categories of big data which are significant for sales and marketing advancements
- Customers: Without accurate customer data no company can survive in this competitive world. The customer big data grouping is most relevant to sales and marketing, comprising of instinctive, behavioural, and transactional customer matrices from sources such as digital marketing campaigns, websites, points of sale, social media, customer surveys, loyalty programs, online communities and forums.
- Operations: This big data grouping comprises of objective matrices that quantify the quality of sales and marketing procedures in relation to resource utilization, asset and budgetary controls.
- Finance: With measuring the company’s financial systems, this big data grouping controls revenue, profits and financial health of the company.
Three categories of big data significant for Human Resources (HR) management
- Hiring: With this big data grouping, HR professionals have enough data driven opportunities to turn out and be more analytical and tactical in attaining good candidates, assisting employers to avoid bad hiring. According to a CareerBuilder survey comprising of 6,000+ HR experts, 27% of employers said that a bad hiring experience cost them around or more than $50,000. Thus, Big data inhibits big mistakes.
- Training: This big data grouping helps employers focus on procuring data associated to training program participation and development, which can further assist them and HR department to enhance effectiveness of their professional programs.
- Retention: By applying big data within workplace, open opportunities to understand why employees leave and stay in organizations. Taking Xerox, as example, using big data and analytics, it reduced its attrition rate at call centers by 20%. By evaluating numerous sources of employee data, HR can more precisely recognize problems that lead to lesser employee commitments, and how they can improve employee engagements.
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As the big data market is swiftly expanding to add mainstream customers, many technologies assure the advancement potentials.
Three categories of big data which are significant for technology advancements
- Predictive analytics: This big data grouping includes software and hardware solutions that consent companies to optimize and deploy predictive data models by evaluating big data sources. It helps to increase business performance and reduce risks by informed business decision making.
- Data virtualization: This big data grouping includes technologies that provides information from multiple data sources, even comprises of big data sources like Hadoop as well as distributed data stores that too in real-time.
- Data integration: This big data grouping includes tools for data scoring for modernizations across solutions like MapReduce, Amazon Elastic MapReduce (EMR), Apache Spark, Apache Hive, Couchbase, Hadoop, MongoDB and Apache Pig.
There are some of the Big Data source types, in compliance to mining techniques that are applied to search your gilt nuggets.
Four categories of big data which are significant for data mining techniques
- Social network profiles: This grouping helps exploring user profiles from LinkedIn, Facebook, Google, Yahoo, and different social media sites, extracting professional profiles and demographics capturing like-minded network information. This requires an API integration for mining and importing pre-defined values and data fields. This technique is known as social data analysis.
- Cloud applications and Software as a Service (SaaS): Software solutions like Netsuite, Salesforce and SuccessFactors represent professional data that’s stored in the cloud. With the help of distributed data integration tools a company can merge this data with the internal data to drive business results.
- Legacy documents: Archives of business and insurance forms, statements, medical records and customer communications are still an untouched resource for data mining. Countless archives are completely filled with old PDF documents and files that comprise of multiple records between customers, organizations and even with the government. Software tools such as Xenos are used for parsing this semi-structured legacy information.
- Data warehouse appliances: EMC Greenplum, Teradata and IBM Netezza tools scrutinize transactional data that is already made ready for analysis through internal operational systems. They help in improving the parsed data and refine final results from the gigantic Big data installations.
As mentioned, data globally is growing at a great pace every year. This rate of growth is alarming for any sales, marketing or technology leader. However, many business managers may feel that this data size has always been big and its correct in one or the other ways. Imagining about the customer data gathered twenty years ago, it included coupon redemption, point of sale data, replies to direct mails or campaigns. Then coming to customer data collected today, it includes online buying data, social media communications, mobile data or Geo location data. Relatively speaking, there’s not much difference.
So, having big data doesn’t mechanically or automatically lead to improved results. Big data and analytics are a secret weapon, which have to be intelligently utilized to derive actionable insights, to boost informed business, technology or human resource decision making, that can further create the real difference.