As businesses become more complex, it has become difficult for organisations to manage their data and deduce useful results out of it. Data Analytics is the process of visualising and maximising data to identify patterns and get actionable insights from it. Every organisation/company creates a massive amount of data on a daily basis. Data analytics is the process of organising this data in clean chunks, to be able to extract valuable information out of it.

The conglomerate term of Data Analytics refers to several applications which perform activities such as:

• Business Intelligence
• Reporting
• Online Analytical Processing
• Decision Management

Data analytics uses the combination of computer skills, statistics, and mathematics to define a predictive model to gain valuable knowledge from data. We at Denizon offer data analytics services so your data gives you insights that can be used for predictive analytics in the future.


Data analytics processes do not just analyse data. Before the actual analysis, the data has to be readied to apply the analytics model. The process of data analytics includes the following:

Data Collection

The data collection layer of data analytics involves identifying the information needed to analyse the data. A data scientist first identifies the data and works with data engineers and IT staffs to assemble it. A number of data sources are analysed and are combined using integration routines to collect usable data.

Data Transformation

After collecting data from different sources, the next step in the process is to make the data usable. This is where the data transformation process comes to play. Data from different sources and different formats are converted to a common format.

Data Cleansing

After the transformation of data, it needs to be cleaned up and profiled in order to meet the analytics standards. Data profiling and data cleansing jobs need to be run on the data, to ensure that the information in the data set is consistent, error-free, and unique. Additional data preparation is done to manipulate and organise the data before data governance policies are applied to meet compliance standards.

Data Analytics

After the data is collected, transformed, filtered, and cleaned, only then does the real data analytics work start. An analytical model is built using predictive modeling tools by a data scientist. The initially created model is tested across a set of test data to check the accuracy of the model. After revisiting the model and working on it, the model is finally run in production against the readied data set.

Data Visualisation

Data analytics can be done on an ongoing basis or to address a specific information need. Analytics applications can be set to trigger business actions automatically or to communicate the results generated by analytical models. Data visualisation helps showcase the results to businesses users using charts or infographics which allows layman users visualise results with ease. Usually, the data visualisation results are fed into dashboard applications that display data on a single screen in real time.


Depending on the business needs, data analytics are of the following four types:

Descriptive Analytics

Descriptive analytics answers the question of what happened. It analyses the available data to learn about past events. It juggles raw data from multiple sources to give insights to the past. Descriptive analytics however only signals that something has happened, without dwelling into the reason why.

Diagnostic Analytics

Diagnostic analytics answers the question of why something happened. After carrying out descriptive analytics, diagnostic analytics allows a business to drill down issues, find out dependencies, and identify patterns. Diagnostic analytics essentially gives in-depth insights into a particular problem to measure risks.

Prescriptive Analytics

Prescriptive Analytics prescribes what action can be taken to eliminate a future problem or to take advantage of a promising trend. The prescriptive analysis uses sophisticated tools and technologies like machine learning, business rules, and algorithms. It helps organisations identify opportunities for repeated activities.

Predictive Analytics

As the name suggests, predictive analytics tells what is likely to happen. It uses the combination of descriptive and diagnostic analytics to detect tendencies and predict future trends based on the analysis. It allows businesses to carry out activities such as triggering targeted marketing activities, weighing in the risks of network failures, and executing cash flow analysis.



Product Development

Data analytics offers knowledge discovery and prediction capabilities, thus helping businesses understand the current state of the business process. Data analytics helps create a solid foundation to predict outcomes and create products that meet future needs. Hence, data analytics plays a major role in shaping the pathway for product development.

Proactiveness and Anticipating Needs

Data analytics helps businesses become proactive by anticipating the needs of their customers. When customers share their data with businesses, they expect the business to know them and provide a seamless experience by anticipating their needs. Hence, data analytics helps provide this experience and ultimately optimise the customer experience.


Data collation combined with analytics helps businesses anticipate market demands and personalise marketing campaigns. Analytics helps businesses determine the exact marketing segment that a customer base will respond to best. Ultimately, personalising and targeting content allows businesses to save money on convincing customers to make purchases.

Operational Efficiency

Data analytics ultimately leads to increased operational efficiency. It helps companies and businesses identify potential opportunities, streamline, and maximise profits. It allows companies to identify the operations that yield the best results and the operations that don’t. It also helps determine the business processes that need to be improved and the error-free processes.

Mitigating Risk and Fraud

Efficient data analytics delivers fraud prevention and organisational security capabilities. Data management, efficient and transparent reporting of fraud incidents, and data analytics tools that offer intrusion detection capabilities make it possible for businesses to mitigate risk and fraud.



Predictive analysis is used in the policing/security domain to predict surges in crimes depending on the time of the year or the time of the day.


Data analytics is also used in transportation fraternity. It helps to forecast the number of people that travel from one place to another or the number of people that might visit a certain location. This helps in planning transportation facilities with ease by forecasting transportation trends.

Fraud and Risk Detection

One of the initial applications of data analytics is the ability to detect fraud and risk. Data analytics helps detect patterns and use those patterns to predict the presence of fraud and risky activities. This fraud and risk detection can be done both in the financial sector as well as the network domain sector. Continuous data collection and analytics helps detect and keep away security issues.

Manage Risk

Risk management is another application of data analytics. Data analytics helps organisations assess data, and evaluate risks based on historical data and failures.

Delivery Logistics

Data analytics has also paved its way into affecting delivery logistics. Logistic companies can use the results from data analytics to find suitable shipping routes, best delivery times, and the most suitable means of transportation.

Web Provision

Internet provision is another application of data analytics. Providing fast internet services in the right place at the right time requires the analysis and prediction of data. Shifting the bandwidth at the right time and location is crucial, and this can only be achieved by using the results obtained from data analytics.

Spending budget

Spending the taxpayers budget or a company budget in the right place at the right time is crucial for success. Data analytics applications help determine the exact areas where the money should be spent and the exact time and condition which reaps the best benefits.

Customer Interactions

Regular customer surveys and gathering information is another crucial application of data analytics. This data helps to understand the business services that are being used more, and the services which need improvement. Customer interactions are another way of understanding and determining what the future of the business must look like.


Data analytics helps travellers optimise their travelling experiences by knowing exactly where to go, what to buy, and where to stay based on the data collected from previous travellers. A traveller’s experiences, preferences, and desires can be used to deliver personalised recommendations, thus leading to a richer, better experience.

Internet /Web Search

Search engines like Google, Bing, Yahoo, AOL, Ask, and DuckDuckgo use data analytics to deliver the best results for any search query put into the search bar. Without data sciences and analytics, it is impossible for search engines to find search results in a split second.

Digital Advertisement

Another area where data analytics comes to play is digital advertisement. Data algorithms control all the digital advertisement details, and hence digital adverts get more CTR (Click-through Rate) than the conventional advertisement methods. The past behaviour of users helps create better-structured advertisements that reap proper benefits.


  • The amount of data being collected

The amount of data being collected by every organisation is extensive. Every organisation receives information about every incident and interaction leaving analysts boggling about a huge amount of interlocking data sets.

The main challenge in collecting data is to deploy a manual process to do so. An automated system to collect data helps to utilise the time spent in collecting data actually to act on it instead.

  • Collecting meaningful data

With a huge amount of data available, it is difficult to make sense of which data is meaningful and which is not. When analysts are overwhelmed with the huge amount of data, it becomes difficult to analyse the entire data set and dig down into the most important insights. Hence, collecting meaningful data is another challenge of data analytics that can pose hazards when it comes to decision-making capabilities.

  • Visually representing data

Data needs to be visually represented in a proper graph, chart, or tabular format for it to be impactful. It is often frustrating to pull information from multiple areas and feed it into a visualisation tool.

  • Analysing data from different sources

Every source of data is spread across different, disjointed sources and are hosted in different systems. This is always not known; hence data collection can at times be incompletely collected and inaccurately analysed. Hence, to cater to this challenge, it is always good to create a centralised system to access data and analyse it from a centralised location.

  • Maintaining the quality of data

Low-quality data is harmful to data analytics. Without high-quality input, one can never obtain a high-quality output and proper decision-making capabilities. Asymmetrical data is another issue when the data in one system does not reflect the changes in the other; it can lead to outdated data.

  • Shortage of skills

Lack of talent and skills is another major challenge for data analytics. For companies without risk departments and human analytics resources, data analytics can be a cumbersome deal. Shortage of skills can be dealt with in two ways; by hiring a skilled resource or by outsourcing data analytics. While the first solution ensures that the organisation has skilled manpower in their hand, the second solution will ensure that even a layman can utilise the data analytics system regardless of their data analytics skills.


As can be understood, data analytics is significant for every organisation to study their data. Small and mid-level organisations often struggle to turn even small data to useful information. Systematic management of data is often cumbersome for these kinds of organisations. There are a lot of benefits associated with outsourcing data analytics with us:

Get dedicated data professionals

We have a team of dedicated data professionals who help you collect your data from various sources, gather facts and figures, review data, and conclude it by offering possible solutions. Our data professionals have years of experience and have adequate knowledge about the proper manipulation of data to give your organisation the proper decision-making abilities.

Free up your internal resources

Even if you have a dedicated team of IT professionals in your organisation, it always makes sense to outsource data analytics in order to free up your internal resources and allow them to focus on other internal tasks at hand. It is also possible that all IT professionals do not have the required, focused skillset when it comes to data analytics, thus leading to incorrect predictions. Hence, outsourcing data analytics with us improves your gain capacity.

Save money

Outsourcing data analytics with us helps you save a huge amount of money invested in hiring specialised software engineers, data analysis experts, project managers, and database specialists for your data analytics needs. Outsourcing data analytics is comparatively more economical for small and mid-sized organisations in this case.

Use specialised tools

Denizon uses specialised tools for analysing, organising, and visualising your data. We deal with complex problems with all organisations that we work with, hence making the process of identifying your problems and their solutions easily. The tools we use, combined with the expertise we have as data analytics experts help make your day to day operations run smoother.

Improve data management

We help you improve your daily, monthly, and yearly data management across different platforms. We help you systemise your data so that the required data can be found at the right place at the right time whenever you need to access it.

Save time

Outsourcing your data analytics needs with us helps you save your time. We know what to do, how to do, and how to manage time in order to get a lot of work done in a short amount of time.

Secure your data

Often organisations focus less on how to secure their crucial data and focus more on how to analyse it and obtain crucial results. We at Denizon make sure that your data is protected the confidentiality of your data is maintained at all times.

Data Analytics

Get advice on the benefits of Data Analytics in terms of Operational Efficiency, Product Development, Risk Mitigation and Fraud.