2.6 Tasks
2.6.2 Talent Strategy
Guide to Business Data Analytics
With the growing demand for analytics professionals, organizations create a strategy to attract and retain analytics professionals and related roles. Competitive compensation is one of the main driving factors in the industry. Several other factors also contribute to recruiting and retaining analytics professionals:
A productive partnership between those providing the business experience (business stakeholders, SMEs, business analysis professionals) and those with the technical skills (data engineers, data analysts, scientists) contribute to the success of any data-driven engagement. These roles work collaboratively to ensure the business context is properly translated to guide the analytics activities appropriately and to find the best ways to obtain value from available data.
For a large organization, the analytics team structure may consist of any or all of the following roles:
Each of these roles has overlapping and complementary skills. Based on the organization’s needs, and criteria such as the size of the initiative, the industry, budget, project plan, and organizational capabilities, multiple roles can be merged to create the right team structure. For example, a business analysis professional with sufficient analytics and business knowledge can play the roles of SME, data analyst, and data journalist, and support data architecture, data engineering, and experience design. The focus area for an organization-level strategy is primarily to determine the guidelines that govern the right team structure for the right initiative.
.2 Learning and Development for Talent Strategy
The complexity of analytics capabilities is changing at an extremely rapid pace, from data management technologies that are capable of handling large volume with high velocity and variety to analytics platforms and toolsets that include complex machine learning and deep learning architectures. Analytics objectives have evolved to be able to include predictive and prescriptive objectives with data science, machine learning, and artificial intelligence playing a increasing role.
To address this rapid transformation, organizations formulate strategies to continuously upgrade their talent in emerging analytics platforms and workbench, big data, and cloud technologies. Many such platforms and technologies are offered as open-source solutions or self-service models. For example, Microsoft’s Azure Machine Learning offers visual modelling for machine learning, which can be used by business professionals directly with a minimal amount of coding. Google's TensorFlow packages help build complex neural networks with a great deal of accuracy. Other utilities can be used to manage unstructured data and create data lakes that hold large amounts of raw data.
In addition to the technical competencies and analytics platforms knowledge, key attributes that ensure analytics success are strong business knowledge and the ability to understand the underlying statistical and mathematical concepts. Many of the analytics solutions in predictive and prescriptive spaces are dependent on the business problem that requires custom analytical models. Strong business and mathematical foundations are essential competencies for analytics solutions.
Communicating the findings in the right way, by developing data stories and visuals, is equally critical to ensure the business outcome. Even if the analytics initiative is producing quality results, if the results are not understood by the stakeholders the entire engagement could be at risk.
When formulating a learning and development strategy, organizations focus on business knowledge, technology and analytics platforms, and data communication and translation competency areas.
.3 Establishing Best Practices
Best practices in analytics engagements are established through the accumulation of experience, lessons learned, and current knowledge and advancements in industry trends. Knowledge about other industries also helps in establishing the best practices.
Establishing best practices in analytics involves identifying a standard set of tools and techniques that work well for the organization, for the types of problems being solved, and for the skill sets and capabilities available. Best practices that provide a set of standard procedures can be recommended to be adopted by organization-wide analytics teams. For example, in establishing best practices, an organization may develop policies to ensure sampling methods between different analytics projects are shared across teams.
Another best practice is maintaining subsets of analytics requirements for re- use or establishing a procedure for securing approval for data access. Whatever the practice is, the motivation for identifying, stating, and developing policies around best practices is to shape analytics in a way that fosters improved performance and moves the organization forward to obtain more value from the investments being made in these initiatives.
- opportunity to work on engaging and exciting initiatives,
- ability to work directly with key decision-makers,
- opportunity to learn the business,
- ability to solve complex business problems,
- access to relevant tools and technologies, and
- a culture that promotes trust and appreciation.
- establishing the right team structure for analytics initiatives,
- having an ability to create an eco-system for learning and development, and
- establishing best practices for analytics initiatives.
A productive partnership between those providing the business experience (business stakeholders, SMEs, business analysis professionals) and those with the technical skills (data engineers, data analysts, scientists) contribute to the success of any data-driven engagement. These roles work collaboratively to ensure the business context is properly translated to guide the analytics activities appropriately and to find the best ways to obtain value from available data.
For a large organization, the analytics team structure may consist of any or all of the following roles:
- Subject matter experts (SMEs): provide specific knowledge of the business sector or specified business domain (for example, finance and human resources).
- Data/solution architect: designs and develops data systems to capture and store data. Generally, does not program systems as that is the role of the data engineer.
- Data engineer: develops and maintains data systems.
- Data scientist: applies advanced technical skills to create and evaluate analytics models to obtain insights from data.
- Data analyst: interprets and analyzes data; may work under the direction of the data scientist.
- Experience designer: develops customized experience prototypes and visual enhancements for different levels of stakeholders that aids in communication and comprehension of analytics results.
- Data journalist: turns results into data stories that can be communicated to different levels of stakeholders within the organization.
- Business analysis professional: establishes the scope for the analytics work and utilizes results to support business decision-making and implementation of the resulting decisions.
Each of these roles has overlapping and complementary skills. Based on the organization’s needs, and criteria such as the size of the initiative, the industry, budget, project plan, and organizational capabilities, multiple roles can be merged to create the right team structure. For example, a business analysis professional with sufficient analytics and business knowledge can play the roles of SME, data analyst, and data journalist, and support data architecture, data engineering, and experience design. The focus area for an organization-level strategy is primarily to determine the guidelines that govern the right team structure for the right initiative.
.2 Learning and Development for Talent Strategy
The complexity of analytics capabilities is changing at an extremely rapid pace, from data management technologies that are capable of handling large volume with high velocity and variety to analytics platforms and toolsets that include complex machine learning and deep learning architectures. Analytics objectives have evolved to be able to include predictive and prescriptive objectives with data science, machine learning, and artificial intelligence playing a increasing role.
To address this rapid transformation, organizations formulate strategies to continuously upgrade their talent in emerging analytics platforms and workbench, big data, and cloud technologies. Many such platforms and technologies are offered as open-source solutions or self-service models. For example, Microsoft’s Azure Machine Learning offers visual modelling for machine learning, which can be used by business professionals directly with a minimal amount of coding. Google's TensorFlow packages help build complex neural networks with a great deal of accuracy. Other utilities can be used to manage unstructured data and create data lakes that hold large amounts of raw data.
In addition to the technical competencies and analytics platforms knowledge, key attributes that ensure analytics success are strong business knowledge and the ability to understand the underlying statistical and mathematical concepts. Many of the analytics solutions in predictive and prescriptive spaces are dependent on the business problem that requires custom analytical models. Strong business and mathematical foundations are essential competencies for analytics solutions.
Communicating the findings in the right way, by developing data stories and visuals, is equally critical to ensure the business outcome. Even if the analytics initiative is producing quality results, if the results are not understood by the stakeholders the entire engagement could be at risk.
When formulating a learning and development strategy, organizations focus on business knowledge, technology and analytics platforms, and data communication and translation competency areas.
.3 Establishing Best Practices
Best practices in analytics engagements are established through the accumulation of experience, lessons learned, and current knowledge and advancements in industry trends. Knowledge about other industries also helps in establishing the best practices.
Establishing best practices in analytics involves identifying a standard set of tools and techniques that work well for the organization, for the types of problems being solved, and for the skill sets and capabilities available. Best practices that provide a set of standard procedures can be recommended to be adopted by organization-wide analytics teams. For example, in establishing best practices, an organization may develop policies to ensure sampling methods between different analytics projects are shared across teams.
Another best practice is maintaining subsets of analytics requirements for re- use or establishing a procedure for securing approval for data access. Whatever the practice is, the motivation for identifying, stating, and developing policies around best practices is to shape analytics in a way that fosters improved performance and moves the organization forward to obtain more value from the investments being made in these initiatives.
