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Efficient data collection methods

The speed and efficiency of data collection methods are truly crucial in today’s rapidly changing environment. What serious business person does not need an innovative approach to help their employees work faster and more cost-effectively?

Long story short, market research used to be expensive and take more time than businesses had to spend. Done the traditional way, manual coding would take months and years to conclude. With big data entering the stage, manual ELT is being replaced by real-time insights that are delivered through fully automated BI and analytics platforms.

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Analytics Strategy

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Analytics Architecture

How to analyse data

That all sounds nice, but how to analyse data in a timely, cost-efficient manner? Data automation is the key. On top of allowing researchers to get faster results, it also provides invaluable insights which are shareable across the board.

For example, imagine that you need one answer only. Instead of digging through all the data, you can simply ask the BI and analytics platform to select best answers for you. These tools perform such operations by allowing customised values. But, what does that mean?

We need to keep in mind that business analytics techniques are many. Roughly, they are divided into basic BI and statistical analysis. Basic BI tools deal with historical data, namely, with past performance. Statistical analysis deals with predicting the future performance by applying statistical algorithms to historical data. Waiting for conclusions from either of the techniques does take time. So, what is the solution?

Business analytics tools are becoming automated rapidly, as the entrance of big data has made it impossible to keep track of all the changes by using manual methods alone. Data science is expanding to add new skill sets and improved algorithms, as to better predict operational performance.

Data automation is beneficial in two ways. Firstly, it is cost-effective and secondly, and it takes considerably less time to generate results. Minerra offers cutting-edge BI analysis tools, coupled with personalised training with one objective in mind: to enable our customers to always be capable of making data-driven decisions.

Ajilius (data warehouse automation tool) and Yellowfin (BI and analytics platform) are fully integrated and customisable, generating interactive dashboards that are shareable across the board. They incorporate both the benefits mentioned above, allowing for a range of customisation options that will help you get detailed answers to your specific questions. Let’s see how that works in practice.

Data Integration

Defining data mining tools

Data mining tools generate patterns inside large sets of structured data. They employ the CRISP-DM methodology (cross-industry standard process for data mining). The process comprises six major steps, as follows:

  • Business understanding – This step focuses on understanding the objectives and requirements for attaining them. It uses the intel to define the data mining problem and draft a preliminary plan.
  • Data understanding – This step implies data collection, followed by a set of operations aimed at understanding the data, identifying problems and generating preliminary insights.
  • Data preparation – This step constitutes a range of operations which are aimed at constructing a structured dataset.
  • Modelling – This step deals with the application of modelling techniques.
  • Evaluation – This step sees models being built and tested. In the end, the best model(s) is being selected.
  • Deployment – The final step that deploys the model into an OS. Once finished, it enables the model to treat new raw data, added afterwards in the same manner as the previous information.

As regards data collection methods, they encompass a set of steps aimed at generating structured data. At a glance, data collection starts with accumulating information from a number of sources. The information is then being stored and shaped into suitable database frameworks, which serve as a model for all additional data to be added later. The final step is information organisation, which sorts raw data and transforms them into structured data.

Shortly put, data mining tools attempt to create patterns capable of predicting future outcomes. The patterns are normally run through data analysis and BI tools, and then used in a number of ways. For example, the insights gained in this way may help a business increase revenues, reduce expenses, and select top performers and best sellers.

Marketing analytics benefits greatly from data automation tools, as the entire lengthy process is shortened — considerably. When dealing with big data, traditional manual methods are ineffective, too slow and way too expensive.

Automated BI and analytics tools come in many shapes and sizes and tackle all aspects of BI. For example, there are statistical analysis tools, self-service analytics platforms, data visualisation tools and so on. Self-service analytics platforms have been gaining popularity of late and are being developed and perfected at a rapid pace. Namely, with businesses being metadata-driven, the need to speedily extract information from all kinds of sources is a must.

Moreover, with such volumes of information, it is important that it be generated in such a way that it will be easy to understand at a glance. Interactive dashboards allowing for additional parameters are the best example of the practice. Yellowfin is one of those. It takes the practice one step further by enabling descriptive attributes to be added.

Finally, research has shown that automation has increased engagement. According to market research expert and managing director of The Future Place consultancy Ray Poynter, “in real-time tracking, automation has enabled the data-gathering to become seamless, and painless, for the participants.” He adds that market research automation can increase productivity, as it allows analysts to focus on thinking, instead of on processing.

Automated data collection methods are here to stay

Automated data collection methods are the future of analysis. On top of data mining tools, data warehousing tools are also being perfected by the minute. Data warehousing extracts data from all sources, loads them into a centralized location and then shapes them into structured data.

Structured data are displayed in an integrated view and are, as such, report-ready. Data warehouses store fact tables and summarized business events. The warehouses analyse historical data, focusing on data changes over time.

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Analytics as a Service

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