Data, Analytics and Insights: What Great Looks Like In The Digital Age
Data helps companies to make real-time decisions in response to customer trends and behaviours. To take advantage of all the opportunities that data can bring, companies need strong maturity across core capability areas covering strategy, people, processes and technology. But what does great look like in this area?
Data Strategy and Organisation: Start With Value
A data strategy exists that defines what data to collect, where to collect it and when to collect. This has executive board buy-in with a dedicated budget. The business starts with value, not data, identifying sources of new value and then determine how data can deliver that value.
A multidisciplinary team is in place that comprises data scientists, data engineers, business analysts, primary researchers, facilitators, project managers, developers, and other specialists. Executive leadership and senior management ensure accountability for all corporate governance processes, measurement methods and controls and the smooth operation of them and adherence to the data strategy. Individuals have clear career paths to promote upskilling with specialised training and development programs. Heavy lifting and repeatable tasks are outsourced to nearshore or offshore partners.
Data Management: Automated Ingestion and Integration
Data sources are comprehensively ingested and integrated in a fully-automated way. Data originates from a variety of disparate sources and these are modelled to derive insights using algorithms which are then applied across multiple channels including external parties (such as social media interactions and customer behaviours) with data quality controls in place across all channels. Unstructured data is classified for analysis using advanced techniques such as natural language processing (NLP), computer vision (images) and speech analytics. New commercialisation opportunities are continually explored as new sources of data are sourced and processed within regulatory and legislative boundaries.
Process and Governance: Collect, Validate, Reconcile
Analytics is infused in all customer interactions and everyday decision making, from marketing and service to products and operations. These are used as a key differentiator and source of competitive advantages such as customer insights (purchasing patterns, profitability, location and device use) customer contact and engagement, competitor behaviours and trends.
Collaboration across the business is significant with a shared vision to continually improve analytics performance and insights. Rigorous and systematic processes and standards are in place for collecting, validating and reconciling data with everything running like a well-oiled machine. Analytics processes are measured for effectiveness and efficiency using performance metrics and regular stakeholder surveys.
Data and analytics governance is in place to ensure adherence to legislation, methodology and to identify model degradation. This comprises frameworks and policies to embed governance strategically into day to day operations. Data privacy and security principles and guidelines are known and properly documented and policies and standards are developed in ways that are both rigorous and diligent. Policymaking and policy compliance operate hand-in-hand.
Advanced Analytics: AI, ML and NLP
An advanced analytics strategy is in place with a clear roadmap of use cases that aligns to the broader business strategy. This includes the deployment of advanced analytical techniques and capabilities such as machine learning (including deep learning and reinforcement learning), artificial intelligence and natural language processing with a combination of supervised and unsupervised learning in place with human and experts in the loop.
Models have been developed and deployed across the business to enable strategies such as customer growth and retention, predict sales and stock levels, manage fraud and disputes, and predict failures. Accuracy and automation of the models are a given, with models routinely executed with no manual intervention required.
The business has adopted an appropriate approach to minimise bias in code and those that wrote it. Collectively these are used to gain insights and enable decision-making in real-time and are continually measured for performance and contribution to commercial success. Techniques and processes are in place to ensure sophisticated models can be explained to everyone across the organisation with training and learning programs in place.
Technology and Tools: A Modern Data Architecture
A modern cloud-based platform has been deployed that uses technologies such as serverless and containers to remove the overheads of configuration and workload management and offer unlimited and automated scalability. The platform architecture is modular, flexible and customised to enable components to be changed or upgraded without impacting the wider platform. Alternative solutions are explored and evaluated regularly to optimise operations rather than creating technology dependencies and this includes outsourcing, open-source and migration to ‘as-a-service’ options.
An appropriate master data management capability and tools ensure data acquisition is consistent, normalised and reconciled regardless of whether the data is unstructured, structured, internal or external. Real-time data streaming capabilities are in place that can continuously process and analyse events against a pattern in real-time using statistical and machine learning techniques to provide rapid insight into what is happening right now to allow proactive actions to be taken within milliseconds of the event occurring. This can be used to improve productivity and customer experiences such as predict issues, make real-time decisions and provide accurate information updates.
Service interfaces expose data and functionality for use by any authorised user to simplify communications, security, and governance to maximise cost efficiency. This gives teams easy-to-access and real-time updates of core data across the whole business without any dependency on data scientists or developers. Platform and tool experts reside outside of any technology team, such as within marketing and sales functions.
At Momentum, we can assess your current data capabilities and produce a plan to improve those that matter the most to your business situation. Contact us for a confidential conversation.
