Data Analytics

Data Analytics Today

Without a doubt, there continues to be an exponential growth of data with the advent and continued development and use of social media, mobile, and web-based applications. Data is everywhere – both structured and unstructured. To give you an idea of what we’re talking about, there is over 2.7 zettabytes of data sitting on servers today and our digital universe is projected to grow to over 180 2.7 zettabytes by 2025 – just a few years from now. That is a 68% compound annual growth rate over the next few years.

Data analytics and business intelligence principals, tools and processes will only become more complex, more valuable as we move ahead. But to be clear on the definition of data analytics – Wikipedia has the definition as:

The process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domain.

Another definition of Source Data Management:

Data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it’s similar in nature to business analytics, another umbrella term for approaches to analyzing data — with the difference that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn’t universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category. Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals — all with the ultimate goal of boosting business performance. Depending on the particular application, the data that’s analyzed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources.

Business Intelligence concept using coloured pyramid design. Processing flow steps: data sources, ETL - data warehouse, OLAP- data mining, data analysis, strategy

Data Analytics Process

The process that surrounds this large area can be broken into the following broad steps:

Data Requirements Definition

Clear definitions of sources, fields, frequency, classifications, attributes, and metadata are identified and agreed upon.

Data Collection

The requirements above are communicated across stakeholders and custodians of the data to agree on access and collection/acquisition of such data and information.

Data Processing / Transformation

Acquired data will need to be organized and structured in preparation of analysis. This could be moving data from rows into columns or image from multiple fields in one application to a single field in the reporting application

Data Cleansing & Enrichment

Identification, correction or removal of errors and duplicates, augmentation/enrichment of data from other systems and sources. For example, removing duplicate customer records and enriching with data from multiple social platforms.

Data Analysis, Modeling, Reporting & Communication

The actual analysis, modeling and repotting from cleansed and enriched data. From the analysis and reporting there will inevitably be commentary and feedback based on analytical model and reporting results from consumers of the aforementioned.