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The Growing Use Of Humanized Big Data

By: Bruce G. Kreeger

 

Big Data is data sets that are so voluminous and complex that traditional data processing application software is inadequate to deal with them. Big Data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, and information privacy. Big Data collects data from traditional and digital sources inside and outside the company that represents a source for ongoing discovery and analysis. There are three dimensions to Big data known as Volume, Variety, and Velocity.

EXAMPLE OF BIG DATA

  • The New York Stock Exchange generates about one terabyte of new trade data per day.
  • Statistics show that 500+ terabytes of new data gets ingested into the databases of social media sites like Facebook every day. This data is mainly generated in photo and video uploads, message exchanges, putting comments, etc.
  • A single Jet engine can generate 10+ terabytes of data in 30 minutes of flight time. With many thousand flights per day, the generation of data reaches up to many Petabytes.
The following characteristics can describe Big Data:

 

  • Volume

Volume is the quantity of generated and stored data. The size of the data determines the value and potential insight and whether it can be considered Big Data or not.

 

  • Variety

Variety is the type and nature of the data. This helps people who analyze it to use the resulting insight effectively.

 

  • Velocity

In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development.

 

  • Variability

Inconsistency of the data set can hamper processes to handle and manage it.

 

  • Veracity

The data quality of captured data can vary greatly, affecting the accurate analysis.

Categories Of ‘Big Data’

Big Data could be found in three forms:
  1. Structured
  2. Unstructured
  3. Semi-structured

Structured

Any data that can be stored, accessed, and processed in a fixed format is termed structured data. Over time, talent in computer science has achieved greater success in developing techniques for working with such kind of data (where the format is well known in advance) and deriving value out of it. However, nowadays, we are foreseeing issues when such data grows to a considerable extent. Typical sizes are being in the range of multiple zetta-bytes.

Unstructured

Any data with an unknown form or structure is classified as unstructured data. In addition to the size being huge, unstructured data poses multiple challenges in its processing for deriving value out of it. A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos, etc. Nowadays, organizations have a wealth of data available to them, but unfortunately, they don’t know how to derive value out of it since this data is in its raw form or unstructured format

Semi-structured

Semi-structured data can contain both forms of data. We can see semi-structured data as a structured format, but it is not defined with e.g., a table definition in relational DBMS. An example of semi-structured data is data represented in an XML file.

 

The trends Internet of Things (IoT) and Industry 4.0 bring intelligence to components and everyday appliances, resulting in a flood of exponentially increasing data. For instance, technically advanced machines are already continuously sending data about the current status in today’s production environment and sending alerts when maintenance is required. Big Data is a technology that collects and analyses the kind of data created by those processes. Big Data’s greatest strength, however – the quantitative foundation – is also its greatest weakness. It is challenging to derive concrete action guidelines or actionable meaning from an analysis of current solutions if the point is to make business decisions based on the collected data. Hence, the current trend is pointed squarely towards “humanized Big Data”: Information should be processed so that non-data scientists can also derive clear answers. “Actionable insights” ̶ from extensive data analyses and use them as a basis for decision making. This requires an approach that is more qualitative than quantitative, as well as a high degree of visualization of the data.

 

In many businesses, the one-sided conversation is taking place around Big Data. Companies recognize that the data being generated by connected devices and consumer activity holds potential. Still, most exchanges are driven by technology platforms that emphasize volume, variety, and velocity, leaving out any discussion of value. To get value from Big Data, you must add contextual information and place analytical capability in the hands of those who need it. In other words, Big Data needs to be “humanized”: taken from the world of bits and bytes and converted into real insight for real business people. Big Data needs to be brought down to earth where people who know business can use it to help drive decisions and unlock its value.

Humanizing Big Data is dependent on two critical elements:

  • Making Big Data easy to access: The ability to access, integrate, and analyze Big Data should be available to the data and business analysts who drive strategic decision-making across the organization.
  • Helping Big Data tell its story: Big Data can provide full stories that drive business value only if it is enriched by the full context of all data available additionally if advanced analytical capabilities can be applied without the need for data science or statistical expertise.
Humanizing Big Data makes it accessible for analysts who operate in today’s enterprise business units, giving them capabilities usually available only to IT. It’s rendering data into information that is easily accessible and highly relevant. It’s making analysis based on Big Data effortless and natural. Instead of relying on specialized skills in programming and statistics, data can be humanized by adding appropriate context and offering straightforward tools for building analytical applications. Humanizing Big Data means working with the data directly so that it tells its story. Having the full story leads to business insight. It also means a new ability for data analysts to hone their craft and expand their ability to do analytics independently. They become, in effect, data artisans. Data is a record or humans have owned set of information since humans exist. Virtually every human activity associated with variables and numbers that can be processed into data.

 

When processed and separated according to the desired benefits and scientifically prepared, these data would be of real value. The human need for higher information directly proportional to the progress of human civilization. However, various incoming data tends to be large and still raw, so it still needs a process known as “data mining.” By using the extraction of the desired pattern of the information will generate the necessary data. If the data has a large and complex dimensionality analyzed manually and traditionally will be challenging to handle and possibly place the data redundancy. If the incoming data continue to grow and correlate with each other, the data will turn into Big Data. Challenges in processing big data are analyzed, how to get the data, the accuracy of the data, searching, sharing, storage, transfer, visualization, sort, update, and private information. When Big data has been processed, a lot of information that can be useful in accordance sorting the selection is done.

EXAMPLE OF HUMANIZED BIG DATA

For example, Big Data for human life (Humanized Big Data) is Google Maps. Since its launch in February 2005, Google Maps has begun to collect data on the satellite imagery of the earth and UX (User Experience) itself. This application’s cycle of recycling continues to grow with their UX, i.e. in the year 2008 with GPS turn-by-turn navigation. In 2013, the collection of information on every street, intersection, bend, even accidents had been collected and accessed by users. Various other services are also being integrated with Google Maps such as Wikipedia, Geocities, Gojek, Uber, Grab, browsers, smartphones, etc. Google Maps is one example of Humanized Big Data that has been widely used nowadays. Its use is still continued to be extracted and integrated with a variety of other applications.

APPLICATION OF BIG DATA

  • Government

The use and adoption of Big Data within governmental processes allows efficiencies in cost, productivity, and innovation but does not come without its flaws. Data analysis often requires multiple government (central and local) parts to work in collaboration and create new and innovative processes to deliver the desired outcome.

 

  • International Development

Research on the practical usage of information and communication technologies for development suggests that Big Data technology can make significant contributions but also presents unique challenges to international growth. Advancements in Big Data analysis offers cost-effective opportunities to improve decision-making. This is particularly true in critical development areas such as health care, employment, economic productivity, crime, security, and natural disaster and resource management. Additionally, user-generated data offers new opportunities to give the unheard a voice. However, longstanding challenges for developing regions such as inadequate technological infrastructure and economic and human resource scarcity exacerbate existing concerns with Big Data such as privacy, flawed methodology, and interoperability issues.

 

  • Manufacturing

Based on TCS 2013 Global Trend Study, improvements in supply planning and product quality provide the most significant benefit of big data for manufacturing. Big data provides an infrastructure for transparency in the manufacturing industry, which can unravel uncertainties such as inconsistent component performance and availability. Predictive manufacturing as a practical approach toward near-zero downtime and transparency requires a vast amount of data and advanced prediction tools to process data into useful information systematically. A conceptual framework of predictive manufacturing begins with data acquisition. Different types of sensory data are available to acquire, such as acoustics, vibration, pressure, current, and voltage and controller data. The vast amount of sensory data, in addition to historical data construct the Big data in manufacturing. The generated Big Data acts as the input into predictive tools and preventive strategies such as Prognostics and Health Management (PHM).

 

  • Healthcare

Big Data analytics has helped healthcare improve by providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms, and patient registries and fragmented point solutions. Some areas of improvement are more aspirational than implemented. The level of data generated within healthcare systems is not trivial. With the added adoption of mhealth, eHealth, and wearable technologies, the volume of data will continue to increase. This includes electronic health record data, imaging data, patient-generated data, sensor data, and other forms of difficult-to-process data. There is now an even greater need for such environments to pay greater attention to data and information quality education.

 

  • Media

To understand how the media utilizes Big Data, it is first necessary to provide some context into the mechanism used for media process. Media and advertising approach Big Data as many actionable points of information about millions of individuals. The industry appears to be moving away from the traditional approach of using specific media environments such as newspapers, magazines, or television shows and instead taps into consumers with technologies that reach targeted people at optimal times in optimal locations. The ultimate aim is to serve or convey a message or content (statistically speaking) in line with the consumer’s mindset. For example, publishing environments are increasingly tailoring messages (advertisements) and content (articles) to appeal to consumers that have been exclusively gleaned through various data-mining activities.

Targeting of consumers (for advertising by marketers)

  • Data Capture

Data journalism publishers and journalists use Big Data tools to provide unique and innovative insights and infographics. Channel 4, the British public-service television broadcaster, is a leader in big data and data analysis.

 

  • Internet of Things (IoT)

Big Data and the IoT work in conjunction. Data extracted from IoT devices provides a mapping of device interconnectivity. The media industry has used such mappings, companies and governments to more accurately target their audience and increase media efficiency. IoT is also increasingly adopted to gather sensory data, and this sensory data has been used in medical and manufacturing contexts.
Kevin Ashton, a digital innovation expert, who is credited with coining the term “Big Data” defines the Internet of Things in this quote: “If we had computers that knew everything there was to know about things, using data they gathered, without any help from us, we would be able to track and count everything, and greatly reduce waste, loss, and cost. We would know when things needed replacing, repairing or recalling, and whether they were fresh or past their best.”

 

  • Information Technology

Especially since 2015, Big Data has come to prominence within Business Operations as a tool to help employees work more efficiently and streamline the collection and distribution of Information Technology (IT). Big Data to resolve IT and data collection issues within an enterprise is called IT Operations Analytics (ITOA). By applying Big Data principles into machine intelligence and deep computing concepts, IT departments can predict potential issues and move to provide solutions before the problems happen. At this time, ITOA businesses were also beginning to play a significant role in systems management by offering platforms that brought individual data silos together and generated insights from the whole system rather than from isolated pockets of data.

 

The value of humanizing Big Data is that your organization can develop tremendous business value with the right tools in place without adding a new layer of skills to the personnel base. Tools such as predictive and spatial analytics have historically been isolated in the hands of the very few. But if organizations genuinely unlock the value of Big Data, they must place powerful tools in the hands of those who will implement the insights they generate. This accelerates innovation and value creation. Through Apteryx, Big Data is “humanized” when sophisticated concepts around data can be delivered to the business user in an understandable format without sacrificing any computational abilities in the background. Apteryx empowers the data artisans in your company to assemble, overlay, and analyze any combination of your enterprise data, market insight, and spatial analytics into a single picture so that you can take action immediately.

Clarity is proud to have been providing Big Data Services to North America for many years. With the addition of our Dotmantech division and an extensive team of developers, we will continue to surpass expectations.

 

Call Clarity at 800-354-4160 today or email us at [email protected]. We are partnered internationally around the globe and we are open seven days a week 8:30 AM to 5:00 PM EST/EDT. http://45.33.92.219 and https://dotmantech.com.

 

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