Re-engage Business to BI

If you have observed users disengaging from your organization business intelligence initiative, you need to take the right steps to regain their trust.

Following are solutions on how to implement business engagement once again.

Why is there disengagement:

·         Longer time to insights, slower performance, or loss of access to information

·         Changes in BI platforms, processes, team roles, and information access

·         Business anxiety of the unknown and wanting analytical capabilities

·         Local BI teams may exist in business units and can be a major disruptive force to the corporate BI initiative

How to re-engage, Key takeaways:

BI and analytics leaders must evolve through the five stages of re-engagement

1.    Add BI with new data sources

2.    Layer BI with Analytics

3.    Enable access to self-service analytics

4.    Support decentralization – collaborative with the local BI teams

5.    Consolidate set of tools, skills and processes; to achieve a unified corporate BI

1.   Add BI with new data sources.

Diagnostic:

·         Identify the data sources that are missing or that need customization to fulfill users' needs, in the data warehouse or reporting layers.

Actions:

·         Broader and easier access to data sources, including those not loaded into the data warehouse

·         Reduced need for highly optimized data models

·         Quicker and easier dashboard design

·         Faster turnaround for user requests by joint development workshops where users sit next to the dashboard developer, providing requirements and feedback in real time as they see results.

·         Expand the range of data sources available on the BI platform. It's not easy to expand the data sources used in BI with traditional development methods. The solution is the reduction of effort on data modeling and report design activities. It will also increase users' satisfaction due to shorter time to insights. The use of a data discovery tool will help achieve this goal. The data models required will be simpler and less structured, with fewer performance optimization layers (such as the need for aggregated tables or cubes), or even based on direct extracts from business applications and other sources.

Points to be taken care while adding new sources:

·         Less effort on data integration

·         Data to be aggregated or properly optimized for queries and analytics processes.

·         The information management infrastructure will get more complex, with ad hoc or less structured processes side-by-side with fully curated data integration processes.

·         ETL process is not the only way to do it. Other scenarios can be used and considered robust enough for production, such as the use of a logical data warehouse (LDW), data lake, self-service data preparation or even loading data directly into the in-memory engine of a data discovery tool.

2.   Layer BI with Analytics

Myth in traditional BI teams

·         The business analytics platform must be a tightly integrated solution with as few components as possible — preferably from a single vendor — to deliver a single version of truth to the organization.
·         Information can only be trusted if stored in the corporate data warehouse and delivered to the information consumer using BI artifacts, such as reports or dashboards.
·         Information created or manipulated by business users will inevitably produce discrepancies through different analysis, leading to wrong decisions and generating chaos in the organization over time.
·         IT's responsibility for information management stops at the BI semantic layer and IT-driven content. Business-driven analytic processes are out of scope and not supported by IT.

New Approach for success

A successful BI need to add on an analytics platform and capabilities to evolve beyond the monolithic mindset.

Following is a tiered architecture layering business outputs over BI to meet Analytical and advanced Analytical needs. To realize the vision of the three tiers and be able to maximize their strengths, BI team needs to deploy new technical capabilities to provide missing analytic styles, improve the usage of existing tools through a better overall integration, and provide common metadata and governance.

The platform tiers must work in conjunction. Table below shows data feeds they share that help create a coherent global BI, Analytics and Data Science platform and, at the same time, how the output differs and caters to different business needs and various tool that are used for addressing these business needs.

	BI Landscape	Analytics Landscape	Data Science Landscape
Input	Transaction Systems	Specific transactions	Detailed System Logs
	Ad Hoc Files	Social Media Sources	Audio
		3rd Party data	Image
		Word, Text files
Output	Reports	Data discovery	Predictive analytics
	Dashboards	Ad hoc queries	Simulations
	Mobile BI	Forecasting	Optimizations
		Location analytics	Big Data
Tools	SAP Business Objects	Tableau	SAS Enterprise Miner
	IBM Cognos	Qlik	IBM SPSS
	Oracle BI	SAP Lumira	R
	Microsoft SSRS	Microsoft SSAS	Cloudera
		Power BI	Hortonworks

BI Landscape

The information portal is the workspace where business users can quickly and easily find the key trusted metrics with which the organization measures its performance. It is usually made of reporting and dashboard capabilities that provide content to information consumers.

Its outputs are the result of a formal development process that includes a business user establishing requirements and a technical specialist implementing them. This can take days depending on complexity and workload. The information can be trusted and is used across the organization, but has low flexibility and reduced associated interactivity capabilities.

Analytics Landscape

The analytics landscape is the workspace used to investigate trends on trusted metrics or to detect patterns in other datasets — from multiple sources — that may turn into opportunities or risks. It is an agile tier to explore information and has access to a broad range of data sources, with limited to no support from technical experts. Toolsets should include a data discovery tool and a number of other capabilities to help business users extract value from information autonomously.

In some cases — namely through the use of more analytics-focused data discovery tools — it can extend to a basic level of predictive analytics and will gain data modeling and more advanced analytic capabilities going forward.

Data Science Landscape

The data science laboratory is the workspace where advanced analytics takes place and is the ideal incubator for big data initiatives. It is a flexible environment where experimentation — with trial and error — is actually encouraged to generate impactful insights for the organization.

A broad set of technical capabilities is expected and often provided by specialized tools with minimal IT integration, meant to deliver agility and the ability to answer unforeseen questions. Users are skilled and experienced, often more than the technical experts in IT. Their toolsets include data mining capabilities, forecasting and other complex statistical and analysis tools.

3.   Enable access to self-service analytics.

Diagnostic:
·         Check users analyzing information in Excel and assess their skills level in information exploration and analytics.

·         Rank the most widely exported datasets to define priorities in self-service analytics data models.

Actions:
·         Data discovery capabilities, including content authoring and ad hoc analysis, must be provided to business users. From the BI team perspective, there will be reductions in data modeling and report design workloads, which will free resources to properly support data discovery in the analytics workbench.
·         Connect the analytics workbench to the existing data warehouse as extensively as possible. If needed, customize and document datasets to make them data discovery-friendly.
·         Provide training and access to the data discovery tool for those requiring authoring or ad hoc information exploration capabilities.
·        Optimize data integration and transformation processes.
·         Start using self-service data preparation tools on the BI team, this should help accelerate the delivery of ad hoc datasets for user-led information exploration processes.

4.   Support decentralization – collaborative with the local BI teams.

Diagnostic:

·         Catalog local data repositories, their data sources, ETL processes and developers.

·         Identify the analytics processes and insights generated and automated by users.

·         Assess users' skills levels on the data warehouse data models and querying capabilities.

Actions:
·         Deploy members from the centralized BI team within business units to improve relations with the business and get a better understanding of their challenges and objectives.
·         Work with business units to identify BI and analytics experts across the organization. Determine available skills and skills gaps and arrange a training program for them. Such a program must include analytics at different levels of specialization and data preparation skills.
·         Start best-practice sharing forums through regular meetings with users that will help build a community effect around BI and analytics.
·         Make local data constructs such as user-built data marts or data extract scripts part of the global information management infrastructure. Consider data federation capabilities to blend user-built data artifacts with corporate data repositories.
·         If needed include write access, querying and unlimited access to data. Bear in mind that if users feel restrained, then the BI team will be discredited and they will find ways around it again.
·         Deploy self-service data preparation capabilities, data integration and transformation tools for more skilled business users. As with the data discovery tools, this will reduce the workload for the BI team — in the data modeling space — and allow it to focus on higher value-added tasks.

5.   Consolidate set of tools, skills and processes in a unified corporate BI initiative.

Diagnostic:
·         List the BI and analytics platforms deployed by users and the capabilities they offer.
·         Identify overlaps, gaps and integration areas between the corporate BI platform and the users' tools.

Actions:
·         The BI team must refrain from following the general rule of replacing users' tools by the one provided by the corporate BI vendor. Instead, they should run assessment processes with the help of business users — including proof-of-concept experimentation — to select the standard tools for the organization.
·         Evolve the information management infrastructure to a logical data warehouse.
·         Unify analytics governance and processes.
·         Further develop the community effect by cloning successful solutions in other business areas or applying them to different business problems. If possible, go as far as rotating experts among business units, with their and HR's support.