Is your Data Analytics Team Prepared for Prime Time?
Business Analytics is an established enterprise function poised for continued focus and investment in the coming years. Based on recent IDC research, the analytics and big data sales will increase by 50% from $122 Billion in 2015 to $180 Billion in 2019. In the same time period, Enterprises would increase their spending on data analytics initiatives from an average of $13.8 Million to over $20 Million. Data analytics and big data tools will become ubiquitous and more affordable due to mass adoptions and competitive pricing.
Enterprises have amassed large structured and unstructured data, including multi-media assets (documents, audio, video). Data collection continues to be more diverse and voluminous. Data from web, social media, Point of Sale, IOT devices, data providers, internal transactions and from the 3rd party sources could be a gold mine for business decisions and drive improved outcomes. Big data technologies such as Hadoop, nosql databases and stream data processing tools are enjoying high interest and good adoption by commercial enterprises.
However, full benefits from investment on data analytics and business intelligence can be realized only through forward thinking and strategic planning for tools, people and processes. The two main risks experienced by data analytics teams are:
Lack of enterprise alignment is often the key hurdle to achieve full potential of the business analytics. Companies do not have the organizational structure to ensure coordinated execution and aligned resources. Abundant data sources bring high volume of complex data, which is hard to integrate if proper taxonomy, data quality standards and integration architecture are not established upfront. Without proper data quality checks, results could be misleading preventing downstream users from using the data for critical decisions.
Leading companies, especially in the consumer sector, have begun to formalize the role of Chief Data Officer (CDO) to drive the enterprise focus across internal and customer processes. Leading a team of data engineers, database developer, business process experts and data scientists, CDO leads accelerating the benefits from investments in data platforms. Successful companies use the data platforms to understand consumer behavior, determine priority of corporate investment, delight customers, sharpen sales and marketing focus, maximize revenue potential, predict customer sentiments, identify at risk customers, preemptively address customer issues and optimize customer advocacy initiatives. CDO, working with product development, sales, marketing and customer care teams, formulates data strategy and common standards that every function and partner agree to and use as in the data collection processes.
Talent availability is the next major challenge. Demand for trained personnel with right business acumen, technical expertize and data sciences practices outpaces the supply. Companies often begin their data analytics projects without adequate in-house expertise. Organizations partner with specialized vendors, which is a quick path to reaping benefit from the advances in information processing. However, for continued success, these efforts must be augmented by in-house staff that understands company business model, operating principles, company culture and customer processes. Data sciences practices is an ongoing journey that needs sustained focus after the initial vendor engagement. Successful predictive analytics practice requires constant model assessment, and continuous adjustment of prediction algorithms and features to remain effective. Enterprises can achieve a sustainable advantage by building the bench strength through a combination of retraining the subject matter experts on their staff and brining new talent with skills in data sciences and big data.
As you plan your 2017 data analytics initiatives, here are some questions for the analytics leaders to ask of their teams to be better equipped for the future :
Enterprises have amassed large structured and unstructured data, including multi-media assets (documents, audio, video). Data collection continues to be more diverse and voluminous. Data from web, social media, Point of Sale, IOT devices, data providers, internal transactions and from the 3rd party sources could be a gold mine for business decisions and drive improved outcomes. Big data technologies such as Hadoop, nosql databases and stream data processing tools are enjoying high interest and good adoption by commercial enterprises.
However, full benefits from investment on data analytics and business intelligence can be realized only through forward thinking and strategic planning for tools, people and processes. The two main risks experienced by data analytics teams are:
- Enterprise vision alignment
- Availability of talent pool.
Lack of enterprise alignment is often the key hurdle to achieve full potential of the business analytics. Companies do not have the organizational structure to ensure coordinated execution and aligned resources. Abundant data sources bring high volume of complex data, which is hard to integrate if proper taxonomy, data quality standards and integration architecture are not established upfront. Without proper data quality checks, results could be misleading preventing downstream users from using the data for critical decisions.
Leading companies, especially in the consumer sector, have begun to formalize the role of Chief Data Officer (CDO) to drive the enterprise focus across internal and customer processes. Leading a team of data engineers, database developer, business process experts and data scientists, CDO leads accelerating the benefits from investments in data platforms. Successful companies use the data platforms to understand consumer behavior, determine priority of corporate investment, delight customers, sharpen sales and marketing focus, maximize revenue potential, predict customer sentiments, identify at risk customers, preemptively address customer issues and optimize customer advocacy initiatives. CDO, working with product development, sales, marketing and customer care teams, formulates data strategy and common standards that every function and partner agree to and use as in the data collection processes.
Talent availability is the next major challenge. Demand for trained personnel with right business acumen, technical expertize and data sciences practices outpaces the supply. Companies often begin their data analytics projects without adequate in-house expertise. Organizations partner with specialized vendors, which is a quick path to reaping benefit from the advances in information processing. However, for continued success, these efforts must be augmented by in-house staff that understands company business model, operating principles, company culture and customer processes. Data sciences practices is an ongoing journey that needs sustained focus after the initial vendor engagement. Successful predictive analytics practice requires constant model assessment, and continuous adjustment of prediction algorithms and features to remain effective. Enterprises can achieve a sustainable advantage by building the bench strength through a combination of retraining the subject matter experts on their staff and brining new talent with skills in data sciences and big data.
As you plan your 2017 data analytics initiatives, here are some questions for the analytics leaders to ask of their teams to be better equipped for the future :
- Are the company leaders aligned on the definition of success of Analytics investment?
- Does the company have a data driven decision process mind set?
- Is right governance structure and cross functional participation in place for data quality?
- Does the business intelligence systems engrained into operational procedures? Is the output of BI systems is trusted by the users and is directly actionable?
- Does the company have right split of analytics spend on legacy and forward looking technology? How much of the spend is allocated to descriptive (e.g. revenue and backlog reporting) versus predictive (e.g. Revenue attainment forecasting) versus prescriptive (e.g. what marketing and sales programs to design)? How much budget is allocated to upgrading the technology platforms?
- Is right set of meaningful metrics established for Business Units and core business processes? Do the matrices correctly measure business outcome and move the proverbial “dial” for the company?
- Does the team have right modern tools for data processing, data curation, visualization, data quality, statistical analysis, and story telling? Is the team trained on technology and data sciences principles?
- Does the team have an Agile process based roadmap to deliver quick wins that establish credibility and build morale?
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