The maturity of the organization’s Analytics methods, tools and competencies is based on exploring the quality data, asking the effective question, exploring the end-to-end business process, building the practical analytics model, measure the KPIs.
The exponential grown of data (both structured and unstructured data) in digital era brings both significant opportunities and risks for contemporary enterprise. Analytics, as one of the most critical components of business capability becomes strategic imperative for business to gain competitive edge and distinctive capability in 21st century.
However, one of the prestigious consulting company made a survey of 600 executives, 8 out of 10 companies have not achieved their goals in analytics. And only 1 out of 12 respondents expressed satisfaction with the return on their investments.
How does modern CIO recognize, prioritize the analytics projects, create the solid analytics roadmap and cultivate the analytics culture to achieve the best result?
(1) Web Analytics: in digital era, every business has web presence, the goals of web analytics include optimized ROI, customer satisfaction, brand enhancement, optimized operational cost, improved business process flow, increasing sales & marketing lead, all stakeholders such as business, IT application development, performance team need work closely to do systemic analytics such as user life cycle analytics or application infrastructure & performance analytics. A strategic analytics plan and measurement is created to evaluate the achievement of the organizations goals.
(2) HR Analytics: today's analytics tools help define the link between “people practices” and performance more effectively, talent analytics culture will fertilize the talent seed, let people grow with organization’s long term strategy, and IT need become HR’s strategic partner, help develop the talent management platform and performance tools which are base on the data merit and measurable performance, tangible influence, rather than political behavior, to retain high performance talent.
(3) Supply Chain Analytics: Effective and efficient supply chains involve constant change and a fluid transfer of information and materials from one stage to the next. To optimize process and to generate maximum efficiencies, the companies need analytics that can provide insight across the entire chain, that can respond to changes, rationalize suppliers, leverage the spending, recognize the bottlenecks, optimize the logistics and execute cost analysis.
(4) Predictive Analytics: it’s the key to become customer-centric, forecast might tell you total quantity of items you may sell, predictive analytics will tell you who are your most profitable customers, which customers might no longer order from you in the future., etc. Predictive analytics help develop customer-tailored solution base on their needs, preferred channel, current situation, and the point of journey. It helps organization to gain the competitive edge and beat competition through customer behavior prediction.
(5) Decision Making Analytics: In today’s digitized global era, decision making has never been more complex and consequential, no matter it’s strategic decision made by executive, operational decision made by mid level manager or tactical decision made by front-liner staffs, intuition and gut-feeling is no sufficient, the biggest challenge and opportunity is how to better-utilize the information on decision-making to carry out the strategy faster and better than competition, the modern decision management may include: business rule, BPM, data management, predictive analytics, event process., etc.
(6) Risk Analytics: Technology and process strategies need focus on building sustainable growth, IT need deliver more value while staying at or under budget, at the same time, IT must mitigate the risks associated with information security, increased regulations and performance & risk management coordination. IT also need take leadership to breakup the data silo, to build up the holistic risk analytics and governance framework .
(7) Big Data Analytics: Companies are increasingly analyzing unstructured data, including voice, text and video, big data also become the metaphor of the large volume of data and information, the characteristic of big data are: volume, velocity, variety, and complexity. Big data bring up both big opportunities and great risk for organizations, via analyzing the big data, company can predict the future trend, listen to customers’ voice and optimize the products and service, on the flip side, security, privacy, data loss protection, data storage, data life cycle management., etc, all data related issues become unprecedented challenges facing in modern organization today.
(8) Contact Center Analytics: the next generation of contact center analytics will streamline the call flow, provides customer insight within and beyond call center forecast, also facilitate the trend analytics, and orchestrate the social enterprise through more integrated platform with all necessary blocks such as social CRM, analytics tools, unified communication, call center., etc, and deliver the customer-centric solution,
(9) Energy Analytics: Green journey for many organization not only means sustainability, but also lead to better profitability, the energy analytics scenario includes to track all parameters relevant to energy, cost and carbon footprints in real-time and analyze these at multiple resolutions to provide the valuable insight.. It’s pre-requisite to ensure data quality and accuracy, deliver insight and enable energy related decision-making to choose the right information., and capture a real-time, fine-grained view of energy, cost and carbon footprints.
(10) Process Analytics: Strategic planning, budgeting, planning and forecasting., etc are process intensive, workflow driven, with top-down, bottom-up and middle-out model, IT engages in the strategic planning process and work with business to deliver the effective analytics solutions. IT will also orchestrate more corporate centric, holistic B&PF to breakdown the divisional based silo planning, to make it part of BI , analytics and performance management strategy
2. Analytics Roadmap
Analytical driven business is the organization that uses analytics extensively and systematically to outthink and out execute the competition. Statistically, high-performance business has five times opportunities to use analytics as distinctive capability.
Developing an analytics roadmap with systematic process for prioritizing and framing the high analytics capabilities to create measurable value and outcome, without roadmap, the organization either underestimate the effort, ignore the pitfalls or simply solve the short-term issues only.
(1) Analytics Structure & Change Management
· Centralized Analytics Structure: Modern IT is transforming from the infrastructure cost center into business enabler and strategic partner, IT can take leadership to framework the centralized analytics team, since data and data management is essential to analytics.
· Decentralized Analytics Structure: Data architects, analysts distribute cross the business functions, the more dynamic CoE-center of excellence is facilitated to share the progress and best practices.
(2) Analytic Business Cases
· Quick Win: Communicate and initiate the business case base on business priorities buy-in & support from shareholders to deliver near-term result
· Strategic Project: Capture the hinder-sight, insight and foresight, enable the business to solve problems timely and approach new market promptly.
· Expansion: Cross-functional, multiple analytic disciplines are required to solve the wide variety of problems an organization faces, while enabling the greatest analytic bandwidth.
· Transformation: Organizational change and analytics capability
expand effort cross-functional track, evaluate and measure the result, the analytics culture has been nurtured, the key processes have been optimized, the organization has been transformed into agile, high-performance business.
(3) Analytics Tips
· Out-of-the-box analytics with a heavy focus on results
· Increased demand by users and continued data model development analytics
· Make it stick: Integrate the analytics practitioners into everyday business rhythms, also commit the measurement
· Agile Analytics: A series of user-driven deliverables, with frequent outputs and check-in
(4) Analytics KPIs & Maturity
The path to analytic maturity has three key areas — leadership, breaking down silos, and developing and keeping talent — are fundamental to fostering an analytics culture.
The maturity of the organization’s Analytics methods, tools and competencies is based on exploring the quality data, asking the effective question, exploring the end-to-end business process, building the practical analytics model, measure the KPIs.
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