Big Data And SMEs: 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 the corporate activity’s data to make better future decisions and design new strategic lines, that is, to improve competitiveness. Today we tell you all about Big Data for SMEs, delving into what data analysis for companies consists of.

The size used to determine if a data set is considered Big Data today is not fully defined and continues to change from moment to moment. However, most professional analysts understand that they are data groups ranging from 30-50 Terabytes and up.

What makes Big Data so useful for many companies is that data analysis often answers questions that not even the organization has asked before. After analyzing the information, companies can identify their problems more understandably. Big Data for SMEs also helps organizations use their data to identify new business opportunities. Using this technology leads to more innovative business strategies, more efficient operations, higher profits, and happier customers.

The importance of data quality

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

Difficulties of Big Data for SMEs

Among the main difficulties that appear in obtaining good quality data, the most important are the following:

1. Many sources and types of data

With the extraction of data from so many different sources, the difficulty of integrating it increases. 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 compiled 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, which complicates the execution of a data quality process that must be framed reasonably. Collecting, cleaning, integrating, and obtaining high-quality data is difficult. A process is needed to transform unstructured types to use them.

3. Lots of volatility

The data changes quickly, making them have very short validity. To solve this, you need to have a 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. Lack of unified data quality standards

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 start until the 1990s. And it was not until 2011 that 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 there are hardly any results today.

The Big Data and the integrity of the data

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

Regarding Big Data for SMBs, recent technological advances have exponentially reduced the cost of storing and computing data, making storing data more accessible and cheaper than ever. Therefore, today’s Big Data is within reach of any Company.

How is Big Data implemented for SMEs?

Although Big Data brings new perspectives that can open the way to greater competitiveness in a particular sector, getting started in this technology requires three important actions:

1. Integrate data

Big Data brings together data from many different sources and applications. Conventional data integration mechanisms are generally not up to the task; thus, new strategies and technologies are required. During the integration process, you need to bring in data, process it, and make sure it’s formatted and available so business analysts can start using it.

2. Data storage

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

3. Data analysis

Investment in Big Data for SMEs pays off when analyzing and using data properly, exploring new opportunities, and building data models using machine learning and artificial intelligence.

Industrial sectors are currently using Big Data

Today Big Data for SMEs helps improve a number of business activities, from customer experience to operations analytics. Next, 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 at the right time. Some establishments, such as resorts or casinos, have only a small chance of turning around a bad customer experience. Big Data allows these companies to collect customer data, apply analytics, and immediately identify potential issues before it’s too late.

2. Health care

Big Data frequently appears 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 packed with crucial data for analytics. That’s why data analytics technology is so important to the healthcare industry. By quickly analyzing large amounts of structured and unstructured information, diagnoses or treatment options can be provided almost immediately.

3. Administration

The Administration will face a significant challenge in the future, maintaining the quality of services and products 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 retailers to understand exactly what they need when they need it. Big Data helps retailers meet these kinds of demands. Using vast amounts of data from customer loyalty programs, shopping habits, and other sources, retailers can gain a deeper understanding of their customers, predict trends, recommend new products, and increase profitability.

5. Manufacturing companies

These companies deploy sensors in their products to receive telemetry data. This information is sometimes used to provide communications, security, and navigation services. This telemetry can further reveal usage patterns, failure rates, and other opportunities for product improvement, which can reduce development and deployment costs.

6. Advertising

The proliferation of smartphones and other GPS devices allows advertisers to target consumers near stores, cafes, or restaurants. This opens up new revenue for service providers and offers many companies the opportunity to acquire new prospects.

7. Call Center

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

8. Fraud detection and prevention

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

9. Financial Markets

The information that comes from the data is used in the transactions of the financial markets, allowing the risk to be evaluated more quickly, thus taking corrective measures.