Big Data and SME : Business Competitiveness Through Data Analysis

Big Data and SME : Business Competitiveness Through Data Analysis

Big Data is the name that describes a large volume of data that can be extracted from the activity of a business. The important thing in this matter is not the amount of data, but what companies can do with that information. It is about analyzing corporate activity data to make better decisions in the future and design new strategic lines, that is, improve competitiveness. Today we tell you everything about Big Data for SMEs , delving into what data analysis for companies consists of.

The size used to determine whether a data set is considered Big Data today is not fully defined and continues to change all the time. However, most professional analysts understand that they are groups of data that range from 30-50 Terabytes onwards.

What makes Big Data so useful for many companies is that data analysis often provides answers to questions that not even the organization itself had previously asked. After analyzing the information, companies are able to identify their own problems in a more understandable way. Big Data for SMEs also helps organizations take advantage of their data to identify new business opportunities. The use of this technology leads to smarter business strategies, more efficient operations, higher profits and happier customers.

The importance of data quality

The advanced technology of Big Data means that to obtain good data quality, analysts have to face multiple challenges.  These are known as 5 Vs: Volume, Speed, Variety, Veracity and Value . These 5 essential characteristics of data to carry out the Big Data process often lead companies to face the problem of whether they are able to extract real, high-quality data in large groups of massive, changing and complicated data.

Difficulties of Big Data for SMEs

Among the main difficulties that appear to obtain good data quality, the most important are the following:

1. Many data sources and types

With data being extracted from so many different sources , the difficulty in integrating it increases. The data sources for Big Data can be very broad:

  • Internet and mobile data (Comments and likes on social networks, marketing campaigns, third-party statistical data, etc.)
  • Data from the Internet of Things
  • Sectoral data collected by specialized companies
  • Experimental data
  • Unstructured data (Documents, videos, audios, etc.)
  • Semi-structured data (Spreadsheets, reports)
  • Structured data (Information stored in the ERP, CRM, etc.)

2. Huge volume of data

As we have already seen, the volume of data is enormous and that complicates the execution of a data quality process that has to be framed within a reasonable time. It is difficult to collect, clean, integrate and obtain high quality data quickly. A process is needed to transform unstructured types so that they can be used.

3. A lot of volatility

Data changes quickly and that makes it have a very short validity. To solve this you need to have very high processing power. Furthermore, if this operation is not done well, there is a risk that conclusions based on erroneous information may be produced.

4. Unified data quality standards are lacking

In 1987 the International Organization for Standardization (ISO) published the ISO 9000 standards that guarantee the quality of products and services. However, the study of data quality standards did not begin until the 1990s. And it was not until 2011 when ISO published the ISO 8000 data quality standards . These standards need to mature and be perfected, since research on data quality in Big Data has only recently begun and today there are hardly any results.

Also Read: 5 Ways To Generate Income With Technology And The Internet

Big Data and the veracity of data

Data has intrinsic value, however, it has no use until that value is discovered. That is why it is crucial to know its veracity. Not only do we have to analyze them to know if they are truthful and real – which is an advantage in itself – but also to launch with them the entire process that requires analysts and executives to ask themselves the right questions, identify patterns, formulate informed hypotheses, and predict behaviors.

Regarding Big Data for SMEs, recent technological advances have exponentially reduced the cost of data storage and computing, making storing data easier and cheaper than ever. Therefore, today’s Big Data is within the reach of any company.

How is Big Data implemented for SMEs?

Although Big Data provides new perspectives that can open the way to greater competitiveness in a certain sector, getting started in this technology requires 3 key and essential actions:

1. Integrate data

Big Data concentrates data from numerous different sources and applications . Conventional data integration mechanisms are generally not up to this task, which is why new strategies and technologies are required. During the integration process, you need to ingest data, process it, and ensure that it is formatted and available so that business analysts can begin using it.

2. Data storage

Big Data requires secure data storage . In addition to storing the data, the processing requirements must be incorporated and the processing engines necessary for said data sets must be available when they are demanded. Today, the cloud as a data storage location is progressively increasing in popularity, because it is compatible with the technological requirements of Big Data and allows new resources to be incorporated as they are needed.

3. Data analysis

Investment in Big Data for SMEs is truly profitable when analyzing and using data appropriately , exploring new opportunities and building data models using machine learning and artificial intelligence.

Also Read: The Revolution Of Today’s Company Is Called Business Intelligence (BI)

Industrial sectors that currently use Big Data

Today, Big Data for SMEs helps improve a series of business activities ranging from customer experience to operations analytics. Below, we show the main sectors in which Big Data is being used:

1. Tourism

Customer satisfaction is key to the tourism industry, but this is often very difficult to measure, especially in a timely manner. Some establishments, such as resorts or casinos, only have a small opportunity to turn around a bad customer experience. Big Data offers these companies the ability to collect customer data, apply analysis and immediately identify potential problems before it is too late.

2. Health care

Big Data appears frequently in the healthcare industry. Patient records, health plans, insurance information, and other types of information can be very difficult and complex to manage, yet this information is full of key data for analytics. This is why data analysis technology is so important for the healthcare sector. By rapidly analyzing large amounts of information – both structured and unstructured – diagnoses or treatment options can be provided almost immediately.

3. Administration

In the future, the Administration will face a great challenge: maintaining the quality of services and productivity , with increasingly tight budgets. This is particularly problematic in everything related to justice. Big Data can help find solutions to streamline operations while giving the Administration a more holistic view of its activity.

4. Retail

Customer service has come a long way in recent years, as savvy shoppers expect stores—retailers—to understand exactly what they need and when they need it. Big Data helps retailers meet these types of demands . Using vast amounts of data from customer loyalty programs, purchasing habits, and other sources, retailers can not only gain a deeper understanding of their customers, but they can also predict trends, recommend new products, and increase their profitability.

5. Manufacturing companies

These companies deploy sensors in their products to receive telemetry data . Sometimes this information is also used to offer communications, security and navigation services. This telemetry can also reveal usage patterns, failure rates, and other product improvement opportunities, which can reduce development and assembly costs.

6. Advertising

The proliferation of smartphones and other GPS devices offers advertisers the opportunity to target consumers when they are near a store, coffee shop or restaurant . This opens up new revenue for service providers and gives many businesses the opportunity to gain new leads.

7. Call Center

Big Data allows the use of the voluminous historical information of a Call Center quickly , in order to improve interaction with the customer and increase their satisfaction.

8. Fraud detection and prevention

Data analytics is being used in industries that process online financial transactions , such as shopping, banking, investing, insurance, and healthcare.

9. Financial Markets

The information that comes from data is used in financial market transactions , allowing risk to be evaluated more quickly, thus allowing corrective measures to be taken.

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