Analytics: Issues With Roles And Organisational Structures

Introduction

This report will demonstrate the Roles of analytics in organisations and will have prospective on issue being caused because of lack of clarity around analytical roles in an organisation and a poorly-defined organisational structure around analytic. Analytics cite to expertise skill sets, application and data exploration on regular basis to have insights that help taking business decisions. Analytics has multiple facets in organisations. It is a set of different forms of techniques and Appling the correct or adequate one to deliver the desired outcomes. Analytics targets on finding new insights with the help of algorithms on data sets or information collected from several sources.

It includes data mining, Prediction techniques, data processing, Artificial Intelligence, quantitative methodologies. Apart from this, knowledge of respective area or domain plays the vital role in organisation analytics portfolio. Organisational data analytics can be interpreted as a combination of “Domain Knowledge and all sort of analytics in a way that drive analytics applications emphasis on retrieving expected results for betterment of Organisation”. Analytics tools is a combination of business outcomes that must achieve.

For example, if its reducing loss, for safety and quality, this should be avoiding costly recalls. apprehension of how to get these expected outcome is the initial step in determining the fashion of each application. Like if we take Insurance fraud, Statistical analysis would not be enough to forecast fraud. A data analytics needs to be focused on text, expertise of key area and capability to picture well organized crime pattern. Information collected through this analysis can be used as an indicator for human decisions or might feed to the fully automation decision system. With this database storage capacity, high speed processor and enhanced software will contribute to get much sophisticated analytic tools.

Following are the key components Or Role of Data analytics in an organisation:

1. Key Domain Experts: A core data analytics needs the domain expertise. More often, organisations buy software’s of analytics assuming it another part of middleware function and generally result as frustrated one when outcomes are hard to attain. As we discussed in fraud predication example, without a domain expert who knows the way looking for fraud- Analytics tools results will be derived blindly.
2. Knowledge Management: Attaining knowledge and proficiency form domain experts wouldn’t be enough. it is just half the battle. Catching this knowledge and robotic its application in a consistent way is the main success factor.
3. Data and Text Mining: There are always new insights in a structured data set and text formulating data and test mining methods. Clusters, trends, patterns and anomalies is understood via data mining ways.
4. Test Analytics: More than 80% of available perception/insights hides in text. Still almost every analytic effort now days doesn’t consider these insights. Organisation analytics should combine the insight from test to be more effective.
5. Predictive models with Statistical Analysis: Statistical Analysis methods is being used to authenticate or validate the patterns and perceptions or assume future expected outcomes.
6. Visual Analytics or Data Visualization: Any movement to help everyone understand the importance of data via capturing it visually. All trends, patterns and connection that can be overlooked in text analytics can be detected easily with visualizing the data.
7. Reporting and Analysis: It can be described as a traditional Business Intelligence however a data analytics report is different from any other reports. It is woven in a way that makes it easier to all different reviewer to skim/fish and discover the topics and level of information that the viewer is interested in.
8. Financial Performance and strategy Management: It consists calculating budgets and planning the way of implementation of that, consolidating the financials, score-carding and strategy management, financial analytics and related reporting.

Below drawn picture features the Key component of Data Analytics including the subsets of component for a better of who do what.

Analytical Organizations – Who Does What?

Data Architect – Designs and structures the databases in which information is to be stored.

Database Administrator – Manages the creation and maintenance of databases, as well as access, stability, and efficiency.

ETL Developer – Implements the Extract, Transform, and Load logic that populates databases.

Database analyst has skills to access a database directly usually by writing SQL queries. Best practices increasingly to locate them with business functions.

Data analyst usually has enough additional context about the business to execute a wide range of analysis on the data and draw a conclusion. This is the central role around which most data analytics functions revolve in many organisations.

Modeller is an extension of a data analyst. Usually performs predictive and prescriptive analytics.

Data scientist is used to describe a lot of different roles (e. g. modeller). A type of blended role. They have a broad analytical skill set with a high degree of context. They sit in the middle of different data manipulation and analysis activities and operate within a process of driving analytics understanding to problem solving.

However, Role of data analytics vary organisation to organisation. In some organisations, the role of data analyst where the Business Analysis and Data Analysis tasks can be performed by the same person, this person should possess skills required for Business Analyst or Data Analyst but in big or more specialized companies this job could be divided in form of two roles which are Data Analyst and Business Analyst.

So, it is a must the analytical roles in an organisation should be clear enough so lead the smooth processing for meeting the expectation and can add value to the organisation.

Issues caused by lack of clarity around analytical roles in an organisation:

Effective conversation is the way to clarity. There are no alternative fixes. When a conversation is effective, miscommunications are either resolved or prevented, and employees feel clear on their roles, goals, and action items. You’ll know a miscommunication has occurred if anyone is unclear. A miscommunication has also occurred if a conversation hasn’t taken place that in fact needs to take place.

Here’s what common issues can occur due to lack of clarity in an organization of Analytics:

Engagement and Relationships

When you don’t know where you stand or where to act, whether it be with a project or another person, you’re not likely to be effective in any capacity. This impact on engagement is expensive for companies. Also tied to engagement, relationships take a hit when there’s a lack of clarity either individually or organization-wide. Employees won’t feel part of a team and won’t be able to build strong connections with each other when their directives or goals are unclear.

Results

It’s a bit of a domino effect. When engagement and relationships are impacted, so are business results, including revenue. Your bottom line, individual goals, and team goals will all be derailed without clarity.

Goals and purpose: If employees or if anyone is unclear of what their intended outcomes are, engagement suffers. Have conversations around expectations with everyone involved can solve this problem asap.

Roles and responsibilities: A formal job description gives employees a general understanding of their role within an organization, but when it comes to day-to-day tasks, that clarity may disappear. This is where delegation conversations are essential. Delegating effectively can create clarity around, for example, whether an individual owns a certain task item or whether they merely need to weigh in on the task.

Issues might be caused by a poorly-defined analytical organisation structure:

There are two ways to structure any analytical organisation:

• Centralized
• Decentralized

And these structures depend on several factors like, size, volume or deadlines of projects. If an organisation is poorly defined that than it can hit in many direct or indirect ways to an analytical organisation which are mentioned below.

1. High Cost to Company.
2. Poor Conversation.
3. Work load balancing.
4. Delay in Results or Deadlines.
5. Unresponsiveness or confusion in who does what.
6. Duplication of data or no data at all if system fails.
7. No Team work or unhealthy work environment.

For example, in my previous organisation I was working with the data management and information system and we use to have a centralized team for all functioning. It wasn’t a too big organisation however having a centralized team always hampered the productivity in terms of deadlines and responsiveness. Back there in the organisation there was manual way to assign tasks to team member which multiple times resulted in workload on single person.

This was taken care later by the management when feedback was shared by team to respective manager and team leader. To solve this issue, there was a queue system designed by the management, in this system when there is any issue or new task occurs, the respective department raise a ticket and that ticket goes to the person who has least tickets open or closed on his account and the ticket had a severity option too.