Building a High-Performing Analytics Team: Strategies for Success

Creating a Data-Driven Culture: Key Steps to Building an Analytics Team

How to Build an Effective Analytics Team: Strategies for Talent and Success

Having BI capabilities have proven to help organizations improve efficiency and stay ahead in overall competitiveness.  With digital transformation taking over sectors, businesses continue to evolve into data driven models. Each day more businesses are focusing on insights from data that drive better decisions and strengthen customer relationships.

Self-service tools such as Tableau, Alteryx, Qlikview, Power BI etc. have introduced a dash of ease into ways businesses convert their data into insights, making business intelligence (BI) initiatives go mainstream.

Talent Acquisition Strategies for Building an Analytics Dream Team

Skill Development: Building Competencies for High-Performing Analytics Teams

Fostering Team Collaboration: Keys to Success for Analytics Teams

Creating a Data-Driven Culture: Essential Elements for High-Performing Teams

Choosing analytics providers, structuring an effective BI ecosystem is easy. The difficulty that decision makers face is building a good quality BI team. In the light of this talent crisis, an accelerating number of companies are motivated to hire any resource they can get, but those approaches mostly go dysfunctional. This not only incurs excessive cost burden for businesses but also makes room of inefficiency leading to the failure of BI endeavors.

Recruiting Top Talent: Strategies for Building an Elite Analytics Team

Developing Analytical Skills: Training Programs for Building a Strong Team

Collaborative Work Environments: Enhancing Team Performance in Analytics

Leveraging Data Insights: Empowering Your Analytics Team for Success

Some Challenges Enterprises Face While Building BI Teams

• Lack of efficient analytics workforce makes way for inevitable competitive lag.
• It is tough for organizations to fulfil skill development needs of their large teams.
• Technical resources are masters of tools but lack the art of business storytelling with analytics.
• Hiring specific SMEs for varied functions of the BI lifecycle drains budget and keeps ROIs in doubt.
• Businesses waste time over manpower fulfilment when they could be leveraging a BI solution for business gains.
• Longer learning curves because of the diversity of tools and the stern need for accuracy in BI ecosystems
• No accountability for the inevitable technical or team related roadblocks that can appear amidst a BI lifecycle.  

The Options

Every business has its unique demands when it comes to building a data workforce. A different approach is needed for each instance and there are several ways, to begin with. The first step being gauging the magnitude of the BI initiative and the expectations from it. Once that’s in place businesses can start to make decisions whether to hire, outsource or augment.

Outsource – If it is right for you

Organizations leveraging data for certain projects or for improvements of business processes need not go that way. If BI is a support initiative, partnering with third parties to execute analytics initiatives is the best bet.

Having a full-time in-house analytics team an attractive proposition but is expensive to manage and especially when companies are new to BI and unsure of ROIs. Outsourcing is an easier, faster and cheaper way to jumpstart analytics endeavors for such businesses.

In-house teams – build and improve over time

Large enterprises having data and analytics as their core business strategy need to put all BI components i.e. people, processes, and platforms in place. Certain business models cannot allow data to seep out of the organization? Other than those reasons, if businesses know that once rolled out BI initiatives will not be taken back, they can go ahead and start hiring specialists to build in-house BI teams.

In-house data teams can determine and control data lineage, but how do businesses ensure a perfect team that is capable of translating data into success? The answer is – if candidates have the aptitude for analytics and know a tool well in the BI arena, they are potentially capable of evolving as BI needs of businesses do and make great hires that cannot be easily poached. The major takeaway here is hiring people who are masters of their tool and constantly provide opportunities to learn and grow so that the team is up to date, always.

Extended data teams – The best model so far

Finding a qualified match for any role in the IT industry is not an easy job but with the diversity of tools, processes, and evolution involved in BI presents some unique roadblocks in hiring the right talent for BI endeavors.

There’s a lot of maturation time in hiring an internal team and training them to attain optimum results from a BI initiative. An extended team can help chart a course through BI endeavors, with a flexibility that these teams can be involved at any stage of the BI lifecycle.

Extended teams came into the picture to free businesses of unruly time and monetary investments. They also eliminate the worries of hiring, training, and fear of losing seasoned BI experts. The best part is that as a customer you can call off the engagement, the minute the projects starts to derail, saving cost and time resources.