Introduction
Analytics is one of the crucial topic that has been given importance in business intelligence. Every business these days has been using analytics as a core department (Isson and Harriott 2013). Moreover, there is a change in the prevalence as it has been shifting from IT to core business units for functioning. The use of analytics is primarily to not only quantify analytics capability but also to manage growth based on fundamental requirements (Widgen, Shaw and Grant 2017). The basic model highlighting the analytics purpose, capabilities and roles
Moreover, there has been many frameworks that provide lead to business performance with the key model as Carnegie-Mellon’s SEI Capability Maturity Model that has 5 levels of analytics assessment starting with Level 1 – Descriptive Analytics, Level 2 – Discovery Analytics, Level 3 – Diagnostic Analytics, Level 4 – Predictive Analytics and Level 5 – Prescriptive Analytics making it incomplete to optimized level (Wells 2016).
This research studies on the Business Analytics Capability Assessment using descriptive analytic on an organization that has been associated in dealing with data analytics.
The organization that will be analysis in the study is Infocentric Pty Ltd. which is known to provide extensive gap to organizations that seek new and creative services to unfold new business opportunities from 7 years (infocentric.com.au 2017). This information management data analytics company has been known for three models in data services that are Analytics, Advisory as well as IP (Intellectual Property).
The organization has completed 700 projects and in total has built its own analytics strategy, even in web and customer analytics through big data insights. The data integrity solutions helps in analyzing the data quality as well (infocentric.com.au 2015). Moreover, the assessment leads to operational data that further detects improved/ reduced processes. However, the quality and the further application of the data is only justified when the organization’s implementation structure of usage go hand in hand with the value score of the staff members, middle and top level management. On the other hand, the combination of experience and skills makes the organization leading provider in converting digital data into insights using innovation to deliver tangible benefits (infocentric.com.au 2016).
The data collection has been done on the responses received from the management of Infocentric Pty Ltd. The sample for the data has been collected using selective sampling such as managers of six departments- Sales and Marketing, HRM, Finance, Analytics Personnel, Customer Service and Business Development (Kitchin 2014). Moreover, the top level management that is CEO and CFO were also 2 of the respondents from the sample. In additional, 2 assistant managers were taken from the data analytics team.
However, the respondents are an asset to the company and that is the reason valuable assets have been considered for the survey undertaken. Conversely, the respondents have valued the score based on their perception and given scores between 1-10 depicting 1 as not satisfactory at all to 10 as highly satisfied scores on the BACA survey including the 11 areas.
The current state of play is using BACA survey where the text is converted into numerical data for the scoring of responses (Wells 2016). The purpose is to enable the use of descriptive statistics of different events and actions in recent scenario.
Choice of Organization
The data analysis has been done using the responses into the mean and median format highlighting the most favorable assessment capability out of the 11 areas. The descriptive statistics evaluates the outcomes of present as well as past events such that quantitative description is applied to the system as well as processes based on the actions and events. The descriptive statistics incorporates all types of central tendency and dispersion methods. As a result, mean and mean as methods is been used for investigating on the responses gathered.
Analytically the BACA survey includes the following table for step by step analysis for all the 11 areas.
|
BACA Survey |
Mean |
|
Data – quality |
5.00 |
|
Data – privacy and security |
4.70 |
|
Data – use |
4.79 |
|
Organization – analytics strategy |
4.73 |
|
Organization – culture |
4.17 |
|
Organization – structure |
4.28 |
|
Process |
4.18 |
|
People – analytics personnel |
4.67 |
|
People – HRM |
3.60 |
|
Technology |
5.53 |
|
Value |
5.12 |
The Radar Chart gives an overview of Infocentric’s key outcomes on the areas where the usage and application of data analytics is essential.
|
Data – quality |
The 10 responses received from the top and middle level management depicts that they trust the quality and integration of the data. On the other hand, the data gives them the feedback to strive for 50% chance to change the quality or enhance the quality on the novel ideas introduced by the combined effort of all levels of business (infocentric.com.au 2016). |
|
Data – privacy and security |
The security of the data is must. However, the responses depict the mean score to be 4.7 which is low is comparison to attain an equal position. Moreover, there have been varied responses amongst the managers |
|
Data – use |
The usage of data is slightly lower than maintaining its quality. However, the organization is in a belief that end to end to use would serve better for the internal as well as external data sources. 4.79 as an average is highly in collaboration with the quality and its security. |
|
Organization – analytics strategy |
The analytics strategy of the organization turns out to be an average of 4.73 which lies in the same benchmark of the radar as others but it needs to focus on its revenue losses as well as customer strategies that gives level of assurance to perform better in targeted retention and acquisition campaigns (infocentric.com.au 2015). |
|
Organization – culture |
The culture of the Infocentric comes out to be lower than most of the average of responses that is 4.17. This states that the company needs to hold on complexity of operations and the rising cost. |
|
Organization – structure |
The structure in the organization is defined and receives an average response score of 4.28 underlining a better analogy of case studies depicted within the structure. |
|
Process |
The process of moving understanding the text to data analytics and further making it digital in nature is highly complex as there is need of wider Big Data platform based on the average response score as 4.17 sharing the same level with organization culture. |
|
People – analytics personnel |
The analytics personnel is the key root to BACA framework as it not only underlines the strengths of the system but also can lead to fall in capability of application development. The average response score is 4.67 in efficiency and risk and mitigation. |
|
People – HRM |
HRM is one business dimension that holds key personnel in the organization. However as the company is highly retail deviated, customer analytics play a crucial role and the dynamics are not well understood (Wang, Kung and Byrd). The same can be scrutinizes with the response average of 3.6. |
|
Technology |
The technology response average is highest that is 5.53 amongst the lot because of the new basics in the data solution that are DevOps build automation solution, CLV segmentation, spend analysis, retention targeting, shrinkage service, spend analysis, web analytics and Big Data insights (infocentric.com.au 2017) |
|
Value |
The organization values its passion for discovering treasures within the data building through complex environment in operations and compliance requirement (Stubbs 2013). 5.12 as average mean outshines the organization’s importance. |
The appraisal of analytical capability divides the organization’s data analytics for the business development and to make close knit relationship with other department’s as well as core members. The business analytics capability assignment through descriptive statistics divides the areas as strengths and weaknesses for the organization in the table given below.
|
Strengths |
Weaknesses |
|
Ø Integrated data quality and the chance of improvement based on innovation. Ø Data use is expedient because of data quality. Ø Although, the organization structure already has a better case studies and its solutions. However, it has scope to accommodate and analyze more of variant data. Ø The analytics personnel is vibrant. Ø Technology and value are the strategic areas that deals with new basics and helps in dealing with complex operation and compliance environment respectively |
Ø Privacy and security of the data needs to be ascertained as there is less acknowledgement amongst the top level management. Ø There are a lot analytical strategies that needs to be revised based on revenue losses and customer strategies. Ø Culture is low for Infocentric as the complexity in data still needs to be dealt. Ø HRM is highly retail deviated and the dynamics are still not channelized. |
The operational system of the organization has not only kept pace with the growth but has also highlighted its unclear volume of errors (infocentric.com.au 2015). The other areas of the organization like technology, value, data quality has been helpful for the organization in improving application performance in business analytics. The two proposals that can further help Infocentric to rise with technology landscape of innovation can be given as:
The organization to solve complex solutions needs to trade-off for the best solution available to them for a better cultural scope. To transform developments from ideas, it needs to be integrated with analytical strategy to achieve organizational goals for the technology landscape to flourish (Clark 2016). Moreover, Infocentric firstly, needs to fix all models of organization and to later strengthen all company benchmark including gap analysis.
The framework of decision making needs to be tactful and based on diversified business operations even when the company is dealing with the out-of-box projects because Infocentric’s technology is fast paced. The challenge is to combat the growing trends, partner, regulatory mandates and realities of supply chain so that the business feels empowered (Chemitiganti 2016). The culture as well as the organizational structure needs to focus on analytical strategies when the privacy and security is restored and there is unity in the decision making actions when the team adopts a silo for data strategy.
Conclusion
To conclude it can be said that Infocentric Pty Ltd. organization is flourishing well as per the BACA survey. However, there is need to focus on complex environment culture with new insights as still there are many errors that needs to be addressed vitally. The proposals made are to add to the recent environment so that resolution can be achieved in data privacy and security and the analytical strategies. The culture of Infocentric is weak and once it is developing all the key areas will add to its specifications.
References
Chemitiganti, V. (2016). Embedding A Culture of Business Analytics into the Enterprise DNA – Hortonworks. [online] Hortonworks. Available at: https://hortonworks.com/blog/embedding-culture-business-analytics-enterprise-dna/ [Accessed 7 May 2017].
Clark, R.M. (2016). Intelligence analysis: a target-centric approach. CQ press.
infocentric.com.au. (2015). Analytics Startegy. [online] Available at: https://www.infocentric.com.au/wp-content/uploads/2015/06/UTILITIES_Analytics-Strategy.pdf [Accessed 7 May 2017].
infocentric.com.au. (2015). Data Quality Integrity Solutions. [online] Available at: https://www.infocentric.com.au/wp-content/uploads/2015/02/Data-Quality-Integrity-Solutions-by-InfoCentric.pdf [Accessed 7 May 2017].
infocentric.com.au. (2015). Enabling Business Outcomes. [online] Available at: https://www.infocentric.com.au/wp-content/uploads/2015/02/InfoCentric-Enabling-Business-Outcomes.pdf [Accessed 7 May 2017].
Infocentric.com.au. (2017). InfoCentric Pty Ltd – Company Website. [online] Available at: https://www.infocentric.com.au/ [Accessed 7 May 2017].
infocentric.com.au. (2015). Managed Services. [online] Available at: https://www.infocentric.com.au/wp-content/uploads/2015/02/InfoCentric-Managed-Services.pdf [Accessed 7 May 2017].
infocentric.com.au. (2016). Data Quality. [online] Available at: https://www.infocentric.com.au/wp-content/uploads/2015/06/UTILITIES_Data-Quality.pdf [Accessed 7 May 2017].
Isson, J. and Harriott, J. (2013). Win with advanced business analytics. 1st ed. Hoboken, N.J: John Wiley & Sons.
Kitchin, R., 2014. The real-time city? Big data and smart urbanism. GeoJournal, 79(1), pp.1-14
Stubbs, E. (2013). The value of business analytics. 1st ed. Hoboken, N.J.: Wiley.
Wells, D. (2016). A Capability Model for Business Analytics: Part 2 – Assessing Analytic Capabilities. [online] Eckerson.com. Available at: https://www.eckerson.com/articles/a-capability-model-for-business-analytics-part-2-assessing-analytic-capabilities [Accessed 7 May 2017].
Wells, D. (2016). A Capability Model for Business Analytics: Part 3 – Using the Capability Assessment. [online] Eckerson.com. Available at: https://www.eckerson.com/articles/a-capability-model-for-business-analytics-part-3-using-the-capability-assessment [Accessed 7 May 2017].
Wang, Y., Kung, L. and Byrd, T.A. (2016). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change.
Vidgen, R., Shaw, S. and Grant, D.B., 2017. Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), pp.626-639.
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