The Central BI and Analytics Team

This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "10 Biggest Big Data Trends."

The central BI and analytics team has a lot of visibility into the different data sets people are using and how they are building their worksheets or dashboards because they are able to see what's popular, what's not, what maybe is coming from sanctioned sources versus we call more unsanctioned sources. They can keep a good eye on things.

So it's enabling people to continue to explore their data in an unfettered manner, but also helps people to be able to get really good visibility into what data is being used to make decisions and standardizing data definitions and those things.

And yeah that was the use case I wanted to highlight. We've have some other really fantastic partners playing in the space because it's one that's very hot right now.

Balance of Self-Service Analytics

Holly: Well, it does speak to helping with self-service and both the agility of the pipeline that the metadata and helping people learn what's available and how to get at it. It speaks to increasing the agility of the pipeline. It really helps with that, and it's the same with the balance between the governance and self-service.

The key and the question for me was the balance, the balance that you want to have more people with access to the data to do more analysis. Then you want to share that, and it becomes a bit counterintuitive. In a lot of governance perspectives, people are very concerned about providing unfettered access to data or wider access to data because people think that they'll get more bad answers on wrong data.

But in fact, counter intuitively, by opening up access to the data and providing self-service and sharing of that data with more and more people, it becomes a better governance control because by putting more transparency into that data and sharing it, you're more likely to catch problems and errors.

So it's a little bit counterintuitive when you're considering opening up data to more and more doing self-service that you're actually -- again enabling and actually improving governance by opening data to a wider audience.

Abhishek: Great, all right. That wraps up our trends, and we've got some really, really good questions I am excited to go through. So folks, please keep adding those while we have seven minutes to answer some of these. So the first one, do you think this variety and volume of data keeps traditional BI away, especially waterfall ways of doing BI projects with OBIEE, MicroStrategy, Business Objects, etc. Will these tools continue to be useful anymore?

I will caveat this with you are asking this question on a InetSoft webinar so this probably is slightly biased point of view, but at the same time our point of view aligns really well to what you see from an analyst. Actually last year they changed the way of organize BI vendors.

Read the top 10 reasons for selecting InetSoft as your BI partner.

They said this sort of traditional waterfall type of BI project, that facilitates more of the report factory, and the top-down waterfall model are essentially things of the past, and they won't see new investments in these areas. So most of those vendors that the question are asked about fell out of their leaders quadrant.

So it's more than just a variety and the volume of types of data that I think are impacting the waterfall model. At the end of the day, there is no way for a single central team be it in IT or in the business, to be able to answer every question that comes up from end users across an organization especially large organizations.

Case Study: Establishing a Central BI and Analytics Team at the Social Security Administration

The SSA handles an immense volume of data from millions of beneficiaries and numerous programs. Traditionally, data analysis was decentralized, with various departments managing their own data and reporting processes. This approach led to inconsistencies, inefficiencies, and difficulties in obtaining a holistic view of the organization's performance. To address these issues, the SSA decided to create a Central BI and Analytics Team, aimed at standardizing data practices, improving analytical capabilities, and enhancing strategic decision-making.

Implementation of the Central BI and Analytics Team

Objectives

The primary objectives of establishing the Central BI and Analytics Team were:

• To centralize data management and analytics functions.
• To standardize data collection, reporting, and analysis processes.
• To enhance the SSA's ability to make data-driven decisions.
• To improve operational efficiency and service delivery.

Key Steps in Implementation

1. Assessment and Planning:
   • Conducting a comprehensive assessment of existing data practices, systems, and capabilities across the SSA.
   • Identifying key areas for improvement and defining the scope and goals of the Central BI and Analytics Team.
2. Team Formation:
   • Recruiting and assembling a team of data scientists, BI analysts, and data engineers with expertise in analytics, data management, and BI tools.
   • Establishing roles and responsibilities for team members, ensuring a balanced mix of skills and experience.
3. Technology and Infrastructure:
   • Implementing a centralized BI platform to consolidate data from various sources and provide advanced analytics capabilities.
   • Investing in modern data warehousing solutions and data integration tools to facilitate seamless data aggregation and analysis.
4. Data Governance and Standardization:
   • Developing and enforcing data governance policies to ensure data quality, consistency, and security.
   • Standardizing data definitions, metrics, and reporting processes across the SSA.
5. Training and Change Management:
   • Providing training programs to SSA staff on the new BI tools, data governance practices, and analytical techniques.
   • Implementing change management strategies to ensure smooth adoption of the new centralized approach.
6. Pilot Projects and Scaling:
   • Launching pilot projects to demonstrate the value of the Central BI and Analytics Team and refine processes.
   • Gradually scaling the team's activities to cover all SSA departments and functions.

Challenges and Solutions

Challenges

1. Cultural Resistance:
   • Some departments were resistant to changing their established data practices and adopting centralized processes.
2. Data Silos:
   • Integrating data from disparate systems and breaking down data silos posed significant technical challenges.
3. Resource Constraints:
   • Limited budget and resources initially constrained the team's ability to fully implement the centralized approach.

Solutions

1. Stakeholder Engagement:
   • Engaging stakeholders early in the process to communicate the benefits of the Central BI and Analytics Team and address concerns.
   • Establishing a steering committee with representatives from key departments to guide the implementation.
2. Phased Implementation:
   • Implementing the centralized approach in phases, starting with pilot projects to demonstrate quick wins and build momentum.
   • Gradually scaling up to include more departments and data sources.
3. Leveraging Existing Resources:
   • Maximizing the use of existing technology and infrastructure to reduce costs.
   • Prioritizing investments in critical areas to ensure the most significant impact.

Outcomes and Benefits

The establishment of the Central BI and Analytics Team at the SSA resulted in several notable outcomes and benefits:

1. Enhanced Data-Driven Decision Making:
   • The SSA now has a centralized, standardized, and reliable source of data, enabling more informed and strategic decision-making.
2. Improved Operational Efficiency:
   • Streamlined data collection, reporting, and analysis processes have led to significant time savings and reduced administrative burdens.
3. Better Service Delivery:
   • Improved analytical capabilities allow the SSA to identify trends and patterns, optimize program delivery, and enhance beneficiary services.
4. Increased Transparency and Accountability:
   • Standardized reporting and data governance practices have increased transparency and accountability across the organization.
5. Scalability and Flexibility:
   • The centralized BI and Analytics Team provides a scalable and flexible framework that can adapt to changing needs and future growth.