Big Data Analytics Initiatives Using Business Intelligence Maturity Model Approach in Public Sector

Business intelligence addressing service quality for big data analytics in public sector

“…Emergence of Big Data in 2011 as the leading chapter in Business Intelligence and Analytics [6], it was represented unusual data source (e.g., sensors, social media, streaming data, video and voice) [20] which is classified as semi-structured and unstructured data [17]. Advanced technologies such as, Hadoop architectures, visualization, predictive and prescriptive analytics) [20], and with combinations of professional skills (e.g., Data Administrator, Data Analysts, Data Engineer and Data Scientists) [17,23,21] are required to be able to make sense out of the data and building block for insights [10].…”

“…Although BI technologies provide historical, current and predictive views of business operations. The credibility of business intelligence technologies is providing analytical reporting from online analytical processing (OLAP) cubes [6]. Meanwhile more complex analytics, data mining and process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, prescriptive analytics and machine learning [25], deep learnings can be achieved from data science space.…”

“…Meanwhile more complex analytics, data mining and process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, prescriptive analytics and machine learning [25], deep learnings can be achieved from data science space. There are several points to be consider before raw data from online transaction processing (OLTP) and other sources of data being feed into the data warehouse [6]. During the transformation process either Extract-Transform-Load (ETL) or Extract-Load-Transform, (ELT), data quality will be comprehended and data reliability will be at stake.…”

“…With Digital transformation on the rise, many enterprises has discovered the traditional way of measuring critical success factor are less effective evaluating the success of a business. Digital traction metrics provides invaluable insights which complements analytic, and it further drives the businesses for using Net Promoter Score (NPS) to measure the engagement of customer against the traditional service quality measurement [3,6]. The metrics provides an avenue for the public sectors to perform valuation on customer satisfaction and pronouncing investment in economics for public enterprise.…”

“…Although overall economic freedom continues to be constrained by systemic shortcomings that damage prospects for more vibrant long-term economic expansion; Public Sector has evolving requirements for utilizing Big Data Analytic, with the recent strategic economic reforms [6,[16][17][18][19][20]. Now with the introduction of Digital Enterprise and Big Data Analytics, the requirements has lead transforming yesterteachings and learning by improving the ability to extract knowledge and obtain insights from large and complex collections of digital data.…”