big data

It’s time to start working with the data and getting benefits

Currently business is going through a digital transformation stage that requires management quick reflexes to technology trends and business process review. The main trends include artificial intelligence, virtual and augmented reality, cloud decisions, big data etc.

Data analytics without a rival is one of the main trends. Technologies development is the reason of data quantity increasing. 90% of the world’s data were created during the last few years, and investments in Big Data amounted to $180 billion. According to BARC research enterprises using Big Data have increased their profit by 8% and cut costs by 10%. Beyond that the following  benefits were noted:

Many companies have already crossed over to digital technologies and generate several GBs of customer data. Such players as Facebook, Amazon, Google are working with big data actively and gaining ground giving priority to the quality of customer service.

In addition, data analytics is a basis for other technologies. For example, artificial intelligence systems learn based on analytics.

Understanding of data analysis and methodology of determining their accuracy gives a possibility to make valid and effective solutions that will lead up business growth and progress. All business solutions must be supported by exact figures and facts that serve the purpose.

Despite the strong performance, there are companies that don’t understand yet how they can start transformation and getting benefits from investments in this sector.

The transformation into data-driven business is a long-term process, that demands investments and following steps realization:

Data Quality and Master Data Management: A brief guide to improving data quality

In the modern data-driven world, the importance of data quality and master data management (MDM) is indisputable. In its pure, chaotic form data is useless, but if it’s of high quality, it can become a tremendous advantage for business leaders. Unfortunately, as the company collects more and more data, the risk of data becoming ‘dirty’ increases. Around 27% of business leaders can’t vouch for the accuracy of their data. Dirty data is the product of human error, duplicate data, the passage of time and other factors. It can undermine the efficiency of analytics and machine learning and cost the company 12% of its revenue.

According to The BI Survey, data quality is one of the biggest problems for BI users since 2002. In this article, we’ll explain what Data Quality and Master Data Management (MDM) is and how to improve it.

Defining Data Quality and Master Data Management

There is no single definition of data quality. Rather, data quality is considered good if it can be used for a certain purpose. It also has a few characteristics. Good quality data is consistent, up-to-date, accurate, complete, valid, and precise. However, a set of data can be good in one context and useless in the other. Knowing how many items the store has sold may be enough to place an order for the next month, but this data doesn’t show whether there was a profit.

This is why we need Master Data Management (MDM). It helps collect data from different sources and coalesce it into a substantive whole. Among other situations, MDM comes in handy when:

…aside from an ERP system, your company works with other SCM or CRM systems and needs consistency across these platforms

…you need to ensure effective cooperation with business partners and fabulous customer experience

…your company needs to merge on-premise and cloud-based systems

Many respondents to the BARC Trend Monitor surveys consider data quality and MDM as one of the most important trends. BI specialists hold the same opinion because they know the popular self-service BI technologies and data discovery tools are valuable only when they’re fed good-quality data.

Steps to improve Data Quality

To enhance data quality and MDM, you must adopt a holistic approach that would address your company’s modus operandi, data quality assurance processes, and technologies. The company ought to define clear responsibilities for data domains (e.g., customer, product, financial figures) and roles. Establishing processes to assure data quality will be easier if you adopt great practices like the Data Quality Cycle. Apt technology is important too, but it’s crucial to focus on the organization and its processes first since they are pertinent to your company’s strategy.

Now let’s look at some concrete steps to improve Data Quality.

1. Assign clear-cut roles

You cannot improve data quality without fostering a culture within your company that recognizes the significance of data for generating insights. This culture includes defining clear roles that will ensure the data is gathered and treated responsibly. Roles help with assigning tasks to certain employees based on their capabilities. The typical roles are:

2. Adopt the Data Quality Cycle  

You cannot check data quality once and then forget about it. This is an ongoing project. That’s why it’s best to do it using an iterative cycle of analyzing, cleansing and monitoring of data. You can break down the cycle into the following phases:

Data Quality goals are defined according to your company’s needs. It will give you a clear understanding of what data you should focus on. To outline these goals, you can start by answering questions like “How can we define the data domain?” or “How can we identify that data is complete?”

After establishing the metrics, you need to use them to analyze data. Here some essential questions are “Is the data valid?”, “Is the data accurate?”, and “How can we measure data values?”

To reach the data quality goals, you need to clean and standardize your data. There is no universal rule on how to do it because every organization has its own standards and regulations.

You can enrich your data using other data such as socio-demographic or geographic information. This way, you’ll develop a comprehensive and more valuable dataset.

As we mentioned earlier, it’s crucial to constantly check and monitor your data since it can quickly become irrelevant or erroneous. Thankfully, there is software that allows you to automatically monitor data according to the pre-defined rules.

3. Use the Right Tools

Most technologies support Data Quality Cycle and offer extensive functionality to assist different user roles. To use such technology to the fullest, you need to integrate the phases of the data quality cycle into the operational processes and match them with a specific role. Carefully chosen software can aid in:

These are just a few examples of modern data management tools’ functions. The full list is quite impressive and should encourage you to prioritize the functions relevant to your business needs.

Better late than never

The complexity of the issue may be intimidating but in the era of digitization, maintaining a high quality of data is a must. Accurate and reliable data can guarantee excellent customer service, intelligent business decisions, and economic prosperity for your company. Like all good things, it requires some effort, but, ultimately, data quality management will pay off.

The significance of big data for complience

In the modern world, where technical progress goes hand in hand with digitization, it’s no surprise that Big Data is on the rise. We produce 2,5 QB of data every day, and this number will skyrocket to an astonishing 163 ZB by 2025, according to an IDC report.

The majority of businesses in virtually every industry have to deal with big data and analyzing it is the hardest part. Considering frequent cyberattacks and recent data breach scandals, the public was outraged and deeply concerned for the privacy of their data, and so stricter regulations to ensure their safety started to appear. As a result, businesses that wish to benefit from the exciting prospects that big data opens must establish a way to analyze data appropriately and avoid breaches by detecting and closing loopholes in time.

What is big data?

The term is not so transparent as it may seem. It can both mean a large volume of structured and unstructured data and ways to analyze, mine and extract value from it. Traditionally, big data is characterized by the three V’s: volume, velocity, and variety.

Volume is the amount of data collected from multiple sources such as social media, real-time IoT sensors, customer databases, business transactions and more.

Variety is the types and formats of data which can be structured like in databases, unstructured (text, images, audio, video files) and semi-structured (web server logs and sensors data).

Velocity refers to the speed at which data is generated and must be processed to deal with business challenges and gain valuable insights. Things like IoT sensors and smart metering necessitate dealing with data in real-time.

Some organizations expand on the mainstream definition by adding another two Vs: veracity and variability. Veracity is the quality of gathered data which can vary greatly due to the sheer number of sources. Bad data can negatively affect analysis and compromise the value of business analytics.

Variability concerns inconsistencies in data, a multitude of data dimensions from numerous data types and sources and unpredictable data load speed.

The companies that deal with big data need to abide by the regulations of different compliance bodies. They must provide detailed reports on the type of data they obtain, how they use it, whether they make it available to vendors, and the employed security measures to avoid data breaches and leaks.

As we mentioned before, it’s not easy to analyze big data. The process calls for highly sophisticated analytical tools and qualified specialists that would guarantee the fulfillment of compliance requirements. Although it sounds overwhelming, the enormous benefits are worth the trouble.

The connection between big data and compliance

Big data impacts the compliance process since companies must keep track of its flow in their systems. Regulatory agencies pay close attention to every stage of data handling, including collection, processing, and storage. The reason for such strict control is to make sure that the company keeps its data out of reach of cybercriminals.

To get the compliance status, the company needs to develop solid risk mitigation strategies. When analyzing data, you’re expected to demonstrate how each of these strategies work and their efficacy. Penetration tests must also become a necessary procedure to protect the company’s infrastructure and data. It involves simulating a malware attack against a system to detect any vulnerabilities. A thorough report on the data security system will help the company to become certified faster.

Unlike the organizations that rely on small data, handling big data during the compliance process is costly, since the company must use sophisticated analysis tools and employ qualified experts. But it’s necessary in order to harness big data power to predict cyberattacks.

The benefits of big data for the compliance process

One of the biggest advantages of big data is its ability to detect fraudulent behavior before it reflects badly on your organization. CSO online report states 84% of organizations use big data to detect cyber threats and report a decline in security breaches. However, 59% noted their agency was still jeopardized at least once a month because of the overwhelming amount of data, lack of the right systems and specialists, and obsolete data.

We’ve already covered the importance of qualified staff and powerful tools, and these are not the most important factors. The most crucial one is the automation of tasks so that data can be sent to analysts without delay.  Using machine learning and AI to develop a predictive analysis model will also greatly fortify the company’s IT infrastructure since it both helps fend off known ransomware but also predicts the new. All this speeds up the compliance process and gains customers’ trust.

Big data also helps to manage the risk which arises from sharing the company data with a third party like vendors. Analyzing their ability to protect your data, you can decide whether to share it or not.

To get a compliance certification, the company must prove its customers are satisfied with the way their data is handled. Applying big data analytics will help understand the customers’ behavior. Based on these insights, the company can adjust its decision making, thus simplifying the compliance process.

If your organization wants to obtain and benefit from compliance certifications, you must adopt big data analytics and develop a preventive compliance strategy instead of the reactive one. It will allow to identify threats from a mile away and take appropriate security measures.

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