The recent IAPA discussion panel on ‘Aligning IT and Analytics to deliver sustainable innovation’, plus a later conversation with fellow panellist, EMC-Greenplum’s James Horton, prompted me to sketch some thoughts on what an Analytics Lab ought to do. The lab is the natural home for Analysts engaged in the narrower definition of Analytics:
The Analytics Lab is an innovation factory which constantly evaluates data, quantitative methods and tools looking for sources of competitive advantage.
- Data: structured and unstructured, sourced from both inside and outside the organisation, established and new.
- Methods: data transformation, and then data mining, machine learning, statistical, mathematical, and other analytical methods.
- Tools: as appropriate to method, from programming languages through to GUI applications, from commodity and open source through to commercial tools.
- Analysts: the lab enables the organisation to evaluate the technical abilities and innovative propensities of its analysts, as well as those on offer from external service providers, without many of the interfering factors present in operationally hardened IT environments.
Its outputs are:
- BI prototypes
- Instantiation candidates
- Identifies data and knowledge gaps: Analysing data and generating insights brings to light new data needs and exposes gaps in knowledge which may impact the business. Additional data may need to be sourced, gathered through survey, collected by tweaking an existing business process, or purchased from a third party. Additional analyses and subject matter expertise may be required to close knowledge gaps.
- Resolves disharmonies: All businesses struggle with ‘different views of the truth’, and it’s often the crunching of data which brings these to light. Disharmonies might be within or between data sets, or between conventional wisdom and the drivers of a model. They could relate to anything from actual observations to tacit assumptions. Resolving such disharmonies—harmonisation—involves identifying, scoping, validating, and correcting them.
These last two are not the core business of Analytics, but they’re important activities, and doing Analytics naturally leads to them. Most organisations don’t explicitly provision for them, but arguably they should. The lab is as good a home for them as any other.
The Analytics Lab services all levels of business, but in different ways:
- Senior Management: through the provision of strategic insights.
- Middle Management and Knowledge Workers: through one-off and/or prototyped BI analyses.
- Frontline Workers: through the identification of instantiation candidates, i.e. deployable operational analytics.
Many analyses typically need to be tried before those which merit instantiation are discovered. Furthermore, “instantiation” doesn’t necessarily mean a repeatable process. It could simply mean the communication of a one-off insight, e.g. “revenue growth is unmistakeably slowing in all but one customer segment” or “the most reliable predictor of a customer’s propensity to churn is their social network membership.” Such insights are typically complex, valuable, but not “actionable” in any deterministic, automatable way.
Other findings are suited to more regularised delivery, for example as managerial decision support through business intelligence.
Some analytical results, in order to be fully leveraged, need to be integrated into frontline business processes. Predictive models which predict customer acquisition or churn, for example, might require integration in sales, marketing, call centre, channel management and customer support processes.
Conduct disciplined, exploratory analyses which repeatedly cycle through the following sorts of questions:
- Is there structure in the data (patterns, trends, relationships, networks, segments, clusters, indicators, drivers, outliers, anomalies)?
- Are there new insights in the data?
- Which models are viable?
- Which variables are important?
- Which variables do we control?
- What are the implications for revenue, cost, risk?
- What data do we want that we don’t have? How could we get it?
- What are the implications of this insight?
- Who is our internal customer for this insight?
- Would this analysis be valuable if provided on an ongoing basis? To whom?
- Into which existing or envisioned business processes should this insight be instantiated?
- Where are there disharmonies in tacit or explicit data and assumptions?
- Which projects, processes and decisions are affected by these disharmonies?
- How do we validate and resolve these disharmonies?
Infrastructure can usefully be separated into the ‘electronic infrastructure’ of hardware and software and the ‘human infrastructure‘ of people, relationships, management and incentives.
- Secure, off-network ‘sandpit area’
- Big storage, big memory, scalable to big data
- Eclectic analytical toolset: commodity, open source, commercial, experimental, in-house
- Snapshots, copies, feeds of all manner of available data sources: pre-ETL, pre-warehouse, post-warehouse, external, web, social media, unstructured. In the context of the lab, the data warehouse is just another source system.
- De-emphasis on repeatable technical processes and compliance with production IT architecture
- Insulated from IT Service Level Agreements and other production / core system / business-as-usual constraints
- Human Resources:
- Analysts: Data scientists
- Management: Validate analysis objectives, ensure that analysts remain focused, performance manage the innovation process.
- Sponsorship from Executive
- Cross-functional relationships with business units: both ‘push’ (business unit as customer) and ‘pull’ (business unit as subject matter expert)
- Close relationship with Strategy function
- ‘Caveat utilitor’ relationship with IT for data provision and tool support
- Various relationships with service providers: vendors, consultants, training and mentoring providers, industry expertise, academia if appropriate
- Performance Management:
- Innovation / Research metrics
- Risk metrics
- Sentiment metrics
- Dimensions of opportunity: Internal, Competitor, Market, Customer, Product, Channel
Related Analyst First posts:
- *Aligning IT and Analytics to deliver sustainable innovation*
- Needles, Haystacks, and Category Errors, or, Where Does Operational Analytics Fit?
- Systemising skepticism
- Assume bad data
- The Economics of Data – Analytics Is… Investing in Data
- Decision support versus decision automation
About usAnalyst First is a new approach to analytics, where tools take a far less important place than the people who perform, manage, request and envision analytics, while analytics is seen as a non-repetitive, exploratory and creative process where the outcome is not known at the start, and only a fraction of efforts are expected to result in success. This is in contrast with a common perception of analytics as IT and process.
Tags in a CloudAIPIO analyst first Analyst First Chapters analytics analytics is not IT arms race environments big data business analytics business intelligence cargo cults collective forecasting commodity and open source tools complexity data decision automation decision support educated buyer EMC-greenplum forecasting HBR holy trinity human infrastructure incentives intelligence model of analytics investing in data lean startup literacy management culture MBAnalytics operational analytics organisational-political considerations Philip Russom Philip Tetlock prediction markets presales R Robin Hanson Strategic Analytics tacit data TDWI Tom Davenport uncertainty uneducated buyer vendors why analyst first