What’s Big Data
When we speak of Big Data we mean data sets or combinations of data sets whose size, complexity (variability) and speed of growth (velocity) make their capture, management, processing or analysis difficult by conventional technologies and tools Such as relational databases and conventional statistics or display packages, within the time needed to be useful.
Although the size used to determine if a given dataset is considered Big Data is not firmly defined and continues to change over time, most analysts and professionals currently refer to datasets ranging from 30-50 Terabytes to several Petabytes.
The complex nature of Big Data is mainly due to the unstructured nature of much of the data generated by modern technologies such as web logs, radio frequency identification (RFID), embedded sensors in devices, machinery, vehicles, Internet searches, social networks like Facebook, laptops, smartphones and other mobile phones, GPS devices and call center records.
In most cases, to effectively use Big Data, it must be combined with structured data (usually from a relational database) of a more conventional business application, such as an ERP (Enterprise Resource Planning) or CRM (Customer Relationship Management).
What makes Big Data so useful to many companies is the fact that it provides answers to many questions that companies did not even know they had. In other words, it provides a reference point. With such a large amount of information, data can be molded or tested in any way the company deems appropriate. In doing so, organizations are able to identify problems in a more understandable way.
Collecting large amounts of data and finding trends within the data allows companies to move much faster, smoothly and efficiently. It also allows them to eliminate problem areas before problems end up with their benefits or reputation.
Big Data analysis helps organizations leverage their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. The most successful companies with Big Data get value in the following ways:
Cost reduction. Large data technologies, such as Hadoop and cloud-based analysis, provide significant cost advantages when it comes to storing large amounts of data, as well as identifying more efficient ways of doing business.
Faster, better decision-making. With Hadoop’s speed and memory analytics, combined with the ability to analyze new data sources, companies can analyze information immediately and make decisions based on what they have learned.
New products and services. With the ability to measure customer needs and satisfaction through analysis comes the power to give customers what they want. With Big Data analytics, more companies are creating new products to meet the needs of customers.
Tourism: Keeping customers happy is key to the tourism industry, but customer satisfaction can be difficult to measure, especially in a timely manner. Resorts and casinos, for example, only have a small chance to turn around a bad customer experience. Big Data analysis gives these companies the ability to collect customer data, apply analysis and immediately identify potential problems before it is too late.
Health Care: Big Data appears in large numbers in the healthcare industry. Patient records, health plans, insurance information, and other types of information can be difficult to manage, but they are full of key information once the analytics are applied. That’s why data analysis technology is so important to health care. By analyzing large amounts of information – both structured and unstructured – quickly, diagnoses or treatment options can be provided almost immediately.
Administration: Management faces a great challenge: maintaining quality and productivity with tight budgets. This is particularly problematic with respect to justice. Technology streamlines operations while giving management a more holistic view of activity.
Retail: Customer service has evolved in recent years as smarter buyers expect retailers to understand exactly what they need, when they need it. Big Data helps retailers meet those demands. Armed with endless amounts of customer loyalty program data, purchasing habits and other sources, retailers not only have a deep understanding of their customers, they can also predict trends, recommend new products and increase profitability.
Manufacturing companies: These deploy sensors in their products to receive telemetry data. This is sometimes used to provide communications, security and navigation services. This telemetry also reveals usage patterns, failure rates, and other product improvement opportunities that can reduce development and assembly costs.
Advertising: The proliferation of smartphones and other GPS devices gives advertisers the opportunity to reach out to consumers when they are near a store, a coffee shop or a restaurant. This opens new revenue for service providers and offers many companies the opportunity to get new prospects.
Other examples of the actual use of Big Data exist in the following areas:
Use of IT logs to improve IT troubleshooting, as well as detection of security breaches, speed, effectiveness and prevention of future events.
Using the voluminous historical information of a Call Center quickly, to improve customer interaction and increase your satisfaction.
Use social media content to improve and more quickly understand customer sentiment and improve products, services and customer interaction.
Detection and prevention of fraud in any industry that processes online financial transactions, such as purchases, banking, investments, insurance and medical care.
Use financial market transaction information to more quickly assess risk and take corrective action.
Big Data Quality Challenges
The special features of Big Data make your data quality face multiple challenges. These are known as 5 Vs: Volume, Velocity, Variety, Veracity and Value, which define the Big Data problem.
These 5 characteristics of big data cause companies to have problems extracting real and high-quality data, from data sets so massive, changing and complicated.
Until the arrival of Big Data, through ETL we could load the structured information that we had stored in our system ERP and CRM, for example. But now, we can upload additional information that is no longer within the company’s domains: comments or likes on social networks, marketing campaign results, third-party statistical data, etc. All this information gives us information that helps us know if our products or services are working well or are having problems.
Some of the challenges Big Data’s data quality faces are:
- Many sources and types of data
With so many sources, data types and complex structures, the difficulty of data integration increases.
The data sources of big data are very broad:
Internet and mobile data.
Internet Data of Things.
Sectoral data compiled by specialized companies.
And the data types are also:
Unstructured data types: documents, videos, audios, etc.
Semi-structured data types: software, spreadsheets, reports.
Structured Data Types
Only 20% of information is structured and this can lead to many errors if we do not undertake a data quality project.
Big Data’s 5 V’s
The Big Data consists of five dimensions that characterize it, known as the 5 V’s Big Data. Let’s see what each of these aspects consists of:
# 1 Volume
Traditionally, the data have been generated manually. Now they come from machines or devices and are generated automatically, so the volume to analyze is massive. This feature of Big Data refers to the size of the amounts of data that are currently generated.
The numbers are overwhelming. And that is that the data produced in the world for two days are equivalent to all those generated before the year 2003. These large volumes of data that occur every time are important technical and analytical challenges for the companies that manage them.
# 2 Velocity
The data flow is massive and constant. In the Big Data environment, data is generated and stored at an unprecedented rate. This large volume causes the data to be out of phase quickly and to lose their value when new data appear.
Companies, therefore, must react very quickly in order to be able to collect, store and process them. The challenge for the technology area is to store and manage large amounts of data that are generated continuously. All other areas must also work at high speed to convert that data into useful information before it loses its value.
# 3 Variety
The origin of the data is highly heterogeneous. They come from multiple media, tools and platforms: cameras, smartphones, cars, GPS systems, social networks, travel registers, bank movements, etc. Unlike a few years ago, when the data that was stored was extracted mainly from spreadsheets and databases.
The data collected can be structured (are easier to manage) or unstructured (in the form of documents, videos, emails, social networks, etc.). Depending on this differentiation, each type of information will be treated differently, through specific tools. The essence of Big Data lies in later combining and configuring data with others.
Each type of information is treated differently, using specific tools, but then the essence of Big Data lies in combining and configuring data with others. It is for this reason that it increases the degree of complexity in the processes of storage and analysis of the data.
# 4 Variability
This feature of Big Data is likely to be the most challenging. The large volume of data that is generated can make us doubt the degree of veracity of all of them, since the great variety of data causes many of them to arrive incomplete or incorrect. This is due to multiple factors, for example, if the data come from different countries or if the suppliers use different formats. These data must be cleaned and analyzed, an incessant activity as new ones are continuously generated. The uncertainty as to the veracity of the data may raise doubts about its quality and availability in the future.
For this reason, companies must ensure that the data they are collecting are valid, that is, they are adequate for the objectives that are intended to be achieved with them.
# 5 Value
This feature represents the most relevant aspect of Big Data. The value generated by the data, once converted into information, can be considered the most important aspect. With this value, companies have the opportunity to make the most of the data to improve their management, define better strategies, gain a clear competitive advantage, make personalized offers to customers, increase the relationship with the public, and much more.