Capabilities-Data Analytics

Data Analytics Capabilities

Organizations that lack data cannot navigate the modern world.  Once data is synthesized into actionable information it empowers organizations to act decisively and intelligently.  Our experience in validating strategies, providing situational awareness and accumulating knowledge from unstructured data has proven valuable to clients across industries.  

Actionable Strategies equips organizations to apply data to run more effectively and with greater agility.  Sustainable data foundations require governance, processes, data quality management and pragmatic use of technologies.  We apply our experience with real-time data, data warehouses, analytical visualization and predictive models in a strategic context.  Our approaches enable clients to turn data into information that drives success with increasing long-term performance.

Data Analytics Program

The client is a $1.1B publicly traded REIT that acquires, manages, expands, and develops office and laboratory space properties.  Data analytics were required by all of the stakeholders, particularly investment, construction, finance and accounting, and senior executives.

After successfully delivering a data warehouse and Essbase analytics project, we continued to deliver against the data analytics roadmap that we defined.  Applying Agile techniques from program management through to product development, yielded business results in 6 months.  The roadmap continued to deliver further analytical capabilities with a predictable cadence.

Business Problem

The business lacked visibility because of the lack of information.  The legacy ERP system provided some raw transactional reporting but this was backward looking and had to be manually manipulated in Excel to produce usable information.  At the root of this problem was the lack of a data foundation and applications to deliver the right information to the right people at the right time.

Solution Approach

Rather than attempting a major data warehouse initiative, we applied an agile approach.  We still utilized good project management practices and developed a program charter which obtained buy-in from senior leaders across the enterprise.

Data Warehouse Pilot

The first project in the program delivered analytical capabilities for finance.  As a publicly-traded REIT it was essential that financial reporting be both accurate and timely. 

Key elements of the pilot, which became best practices for future projects:

    • Scope control
    • Agile approaches AND strong project management

Successful Project

Once the pilot garnered excitement, our mandate to drive the program forward was solidified.  While the

client was concerned about results rather than the Agile techniques used, they were critical to rapid success. 

Evolutionary Program

Continuing with the Agile approach, the next steps in the program were defined.  Once again, we applied solid program management practices and actively engaged both stakeholders and detractors to maintain alignment and reduce resistance.

The next detailed part of the roadmap delivered high value analytics that were achievable.  For long running programs, our objective is to establish a steady state that can be brought in-house and managed by the client.

Key Challenges

The program was faced with a number of key challenges.  While data modeling was difficult, it was completed in a timely fashion.  The relative immaturity of the organization with respect to data and analytics posed several constraints.

Master Data Management

We had to educate the client around data modeling and master data.  They were very receptive and agreed to add a separate project to address their issues with master data.

Data Quality

The presumption by the client that data was clean eventually eroded as rapid prototypes revealed data quality issues.  

Entitlements

As a publicly-traded company, the client had many data assets to protect.  Developing an entitlements model was time-consuming but necessary.  The model was propagated back into HR and IT processes.  

Requirements

Unaccustomed to enterprise programs, the client had difficulty reaching consensus.  Being pragmatic, we delivered building blocks such as dashboard components which could then easily be rolled up into enterprise-wide business views.  

Business Results

Applying Agile techniques combined with pragmatic program and project management discipline quickly delivered tangible business results.  This established the foundation for a data analytics program that grew in functionality and footprint to include Oracle Business Intelligence.  Subsequent business projects began delivering value based on a solid and expanding foundation.  Knowledge transfer, documented and automated processes, and extensive collaboration enabled the client to move rapidly up the maturity curve.  This facilitated an eventual transition to in-house teams.

Enterprise Business Intelligence Framework

Evolving market conditions in all industries have brought the need for insight into the business into much sharper focus. By applying Lean principles to attaining better insight, organizations achieve high impact results.

    • Strategic alignment of measurements
    • Performance management driven by results
    • Strategic insight into the enterprise
    • Agility in execution to drive immediate value

Actionable Strategies applies a strategic framework to ensure that the front-line delivery aligns to business objectives.  Simple end user business intelligence tools often rely on incomplete, incongruous or incorrect data.  The focus of enterprise BI is on delivering correct data in a consumable form at different levels of the organization.

Data Science 

This industry leading company provides processing of HR data and outsourcing on a global basis.  It has amassed the largest independent data sets encompassing just under a million companies and over 30 million people.  Despite having 60 years of history, the company was lagging smaller competitors in the area of analytical capabilities.  

Actionable Strategies built the foundational organization, processes and technology models used by the client today to provide products, solutions and advisory services to their customer base.  The initial data science work enabled the client to understand what assets could be delivered to the market and also used internally.  

Solution Approach

An evolutionary approach enabled the client to move from Plan and Pilot activities to the Growth stage. 

  • Pilot deployment delivered initial capabilities to support exploration by data scientists
  • Initial exploration supported pilot product development and led to an initial release of an analytics product
  • Subsequent efforts led to a structured data repository supporting sustainable product development and exploration by internal users

Key Technology Approaches

A sustainable model was established and built incrementally.

  • Shared Hadoop Data Lake for all stakeholders
  • Anonymized personally identifiable information, protecting privacy and avoiding data spills
  • Aggregation of data for privacy but also to facilitate very fast analytical performance
  • Common interfaces (e.g., JSON) to enable product development across platforms
  • Support for enterprise standard toolsets (R, Oracle, Cognos, D3, HighCharts) and end-user exploration tools (Cognos, Tableau, Microsoft Power BI and Excel)

Initial data science work evolved into delivering a mature data science foundation used by different stakeholders with varying technical skills.

Data Quality Management 

This global real estate firm invests in best-in-class campuses for science and technology in the most desirable urban locations.  Their total market capitalization is $11.1 billion and they operate an asset base of over 25 million square feet with 6 million square feet of new development in the pipeline.

As executive and front-line leadership grew more sophisticated in the needs for analytics, data quality issues arose as more advanced visualizations increased the consumption of data cross the organization.  Quality issued had been previously masked as data tended to remain in operational silos such as ERP, with summarized information used for decision-making. 

Situation

The client uses an enterprise resource planning platform to house data for every major function of the business.  However, each function produced and consumed data as appropriate for their individual use.  Major functions such as investments, asset management and financial reporting treated information in very different fashions.

As executive and front-line leadership grew more sophisticated in the needs for analytics, data quality issues arose.  These had been previously masked as data tended to remain in operational silos with summarized information used for decision-making. 

Approach

Actionable Strategies dealt with initial resistance to acknowledging data quality issues.  We have found this to be commonplace as data may be fit-for-purpose but not usable as-is in the enterprise.

Data governance: Data governance was not in place and needed to be established using Actionable Strategies’ proven framework.

Data owners: One foundational activity was to establish data owners who “produce” information for others to consume.  Their upstream position in the information value chain empowers them to maintain data quality while obligating them to produce it in a fashion that conforms to downstream use.  Individual contributors were educated on how their data was consumed and how to execute data quality processes.

Data stewards: Data stewards with subject matter expertise were identified.  They were educated in the relevant value chain, which included both process and data knowledge.  At this particular client, they were empowered to directly affect operational change to maintain data quality on behalf of data owners.

Data quality processes: Effective procedures were established to ensure data quality.  While many of these were supported by technology, many manual processes persisted.  Data quality checks were embedded into operational processes.  These data quality checks provided metrics and reports to data owners who then acted to remediate quality problems.

Architecture: An architecture that met immediate and future data quality needs was defined.  This included automation of workflow and data ingestion.

Results

Known data quality issues were quickly remediated allowing data to flow into multiple analytics platforms.  Data quality problems were detected upstream and addressed as identified compared to months of lag time for discovery and eventual remediation.  While larger data sets were surfaced, compliance was simultaneously improved, including:

  • Protection of personally identifiable information (PII)
  • Protection of protected health information (PHI)
  • Security around non-public financial data
  • Security around proprietary and confidential data
  • Integrity of operational, financial and management data

Big Data Product Development

Click here to view the case study of this early stage media company that big data from set-top boxes and out sources to drive media planning. We linked the ads viewed by demographic groups and linked them to eventual purchases.  This allowed media purchases to become highly targeted.

As a result of the successful product launch, the client grew as a leader in the field.  Eventually, it was purchased by a large media player leading to a highly successful and profitable exit for investors.

Data Warehouse and Data Analytics 

Click here for the case study of a data warehouse and analytics program.  The client is a global real estate services company that operates a portfolio of properties and development projects.  It is fully-integrated, developing and managing premier assets focused on the tenant experience. 

The company has $202B in assets and 19,000 employees managing over 600 properties. 30 more projects, comprising 40M square feet, are under development.  The portfolio contains 450M square feet of commercial properties, over 17,000 apartments and 170 retail destinations.

Actionable Strategies was engaged to manage a program to deliver a data warehouse, business intelligence and data governance while improving PMO capabilities.

Next Generation Dashboards: Navigation vs. Rear View Mirrors

Leaders seeking to drive business performance need a clear picture of their organization to help guide effective decision making.  Dashboards can provide guidance when properly aligned and constructed.  Unfortunately, dashboards often fail to provide appropriate insight for a number of reasons. In fact, 80% of analytics insights fail to deliver business outcomes.

This white paper outlines approaches to construct business-aligned dashboards.  The approaches provide guidance on building forward-looking analytics and cover relevant business topics.

    • Meaningful business measures
    • Time series data
    • Focused metrics
    • Customer measures
    • Benchmarks

Dashboards can help leaders make better decisions and compete more effectively.  Success is a result of utilizing actionable information.