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The role of technology in data management

 The amount of data we create is expanding every day. Every moment we conduct a search, visit a website or comment on a post, complete a purchase, we add to that vast ocean of information. information can be a valuable asset. We can utilize it only if we know how to tap into and extract the relevant bits for our use. It is a well-known fact that the volume of data has increased way faster than most organisations’ ability to process it. 

According to the latest study, this excessive amount of data has crippled some executives, with 61% of managers reporting information overload at their workplace. This overload has led more than 50% of them to ignore current data in their decision-making process because they did not have a mode to transfer that data into actionable information.

Data Management

It reaches the question: What decisions can your organisation make if you have all the information you need? The data management system provides a way to utilise data in its various forms. 

The strategic implementation of data management technologies can help your organisation achieve:

  • More knowledgeable and quick business decisions 
  • Increased productivity
  • Improved organisational consistency
  • Greater communication and collaborations

If you fail to take advantage of the opportunities the data management technology provides, you can place your organisation at risk of:

  • Organisational waste
  • Decreased productivity
  • Conflicting Analysis and Information 
  • Missed opportunities

If we talk about the data managers, they are tackling the issues of distributing and managing accurate and timely data across their companies with highly automated technology solutions. While technology plays a significant role in data management, there are other issues to consider, including procurement, product selection, ongoing business development, and project management. 

Here, you will explore some of the significant data sources, describe that data, describe some of the techniques used to carry it and provide real-life examples of how organisations use their data to expand their customer base.

Where is the data coming from?

Data is created using a multitude of different sources. Data analysts most likely define data as structured or unstructured. Structured data is highly organised information uploaded into databases, quickly indexed, and detected by algorithms and search operations. Structured data usually consists of objective and numerical information that does not need interpretation. Some sources of structured data include:

Machine and Sensor Data

  • Smart utility meters
  • Data generated by functional devices like network-connected home appliances
  • Monitored processes of factory machinery

Transactional Data

  • Online purchases or E-commerce
  • Point of sale transactions
  • Behavioural transactions like clickstream data 

Mobile Data

  • Data recorded or generated by in-app transactions
  • Web server logs
  • Updates in locations and status

Unstructured data is often language-based and human-generated data and likely less focused and harder to categorise. Here are a few examples of unstructured data:

  • Wikis and blogs
  • Audio and video files
  • Information contained in emails
  • Postings on Instagram, Facebook, Twitter and other social media accounts

Structured data describe moments in time that are different and accountable, i.e. at what point a machine reached a certain production level or where and when a purchase took place. Unstructured data reveals more about emotion, opinion, and relationships between products and customers. 

How will you sift through the data?

As we have identified some of the sources of data, what techniques are available out there to transform it into a piece of actionable knowledge? Here are given some of the most common techniques, namely, text mining and data mining.

Data Mining

Data mining processes assess the massive amount of data searching for consistent relationships and patterns. Then they further attempt to validate these potential patterns by applying them to new data subsets. The two primary tasks of data mining techniques are the formation of predictive and descriptive powers. 

The role of descriptive powers is to find interesting and interpretable trends in the available data sets. On the other hand, predictive powers use that information to deduce unknown values into the future. Some of the fundamental techniques used in data mining are classification, association, and sequential patterns.

  • Classification: Classification means defining multiple attributes used to identify any item/substance or a customer. For example, one can classify a potential pool of customers based on income, age, and zip code.
  • Association: Association means a correlation between two items. Example: In a study of customers purchasing habits, one can note that they buy cream and coffee together, thus creating an association between the two products.
  • Sequential Patterns: It is used to analyse long-term data to find frequent occurrences of similar events. It is evident when customers buy or search for similar products at particular times in a year.

Text Mining

Text mining technologies analyses various documents, zeroes in on vital concepts, words or phrases used. The critical text mining techniques include Entity Extraction and Sentiment Analysis.

  • Entity Extraction: This means identifying and classifying crucial elements like people, places, or organisations within a text into defined segments. It helps an analyst to review a quick and structured representation of the document’s contents.
  • Sentiment Analysis: Identifying and categorising opinions expressed in the form of text to determine whether the writer’s reaction is positive, neutral, or negative.

In conclusion, data managers are tackling the issues of distributing and managing accurate and timely data across organisations with the help of highly automated technological solutions. While technology plays an essential role in data management, there are other issues to consider, such as procurement, product selection, ongoing business development, and project management. In this way, technology plays a crucial role in data management. 




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