How Enterprises Are Using Analytics to Improve Cross-Department Collaboration
Data is now the life blood of organisations when it comes to making organisational decisions in this Digital Age and not just an advantage over one’s competition but rather the driving force behind organisational decision making. Today, businesses are using Analytics not only to prepare and produce reports or dashboards but also to develop and implement an enterprise-wide analytics strategy that connects the functions of different departments towards a common purpose. Through Analytics, the ability to gain insight for the purpose of decision making is now available to everyone and provides an opportunity to eliminate silos between teams, foster mutual understanding and promote coordinated action.
Analytics for Cross Functional Teams
Traditionally, businesses have been organised into departmental silos, e.g. Sales and Marketing, Finance, Operations, HR and Customer Success, in which each department collects, maintains and manages its own data set independently. While the above process may benefit each team in its operations, it does hinder the ability to see and assess performance across departments, which leads to delays in decision making and differing interpretations of how well each department is performing. A unified analytics strategy allows organisations to break down this structure and develop a shared foundation of data, which enables all teams to use the same base of information for making decisions based on data.
From Data Silos to Shared Insights
With an implemented Analytics Strategy, organisations will be able to consolidate databases from multiple data sources (i.e. data warehouse, data mart), convert the raw data into actionable intelligence and provide each team involved in the strategy with a common language of metrics to evaluate their successes toward achieving the original goal.
A major challenge to successful cross-team collaboration is the existence of “data silos”, which refers to information that has been created in various formats, systems, and processes without any connection to other departments. By utilizing cross-departmental analytic tools, these silos can be eliminated through the development of centralized databases and cross-departmental analytic platforms that allow all partners involved in the collaboration process access to the same data streams.
Through the creation of a unified dashboard that displays customer lifetime value, operational efficiency and other key performance indicators (KPIs) in real-time across the various teams, we can improve the overall speed of product development and shorten the product feedback cycle. With both the customer service and product development teams viewing the same KPI metrics, product development can be improved through the use of customer feedback.
In addition, several organisations are building real-time dashboards which connect performance KPIs across interdepartmental departments for the benefit of accountability, quicker decision making and enhanced coordination between departments. One such example is a digital dashboard platform designed to eliminate data silos and foster greater governance through providing timely information to users and monitoring processes effectively. (Big News Network)
Let’s Talk About Real-time Coordination
Having the ability to see what is currently happening with your business’ performance isn’t just a “nice to have” anymore; it’s an absolute requirement. The development of advanced analytics technology makes it possible for businesses to consume and analyse real-time information about what’s happening in their business.
The way businesses are using analytics is changing how different departments in a business unit connect and communicate across the organisational structure. For instance, “AI-Powered Low Code Platforms” are helping to bridge the gap between Flight Operations and Customer Service functions of the airline industry.
The ways in which businesses now use analytics to communicate information about their day-to-day operations are helping reduce the misunderstandings and miscommunications businesses traditionally experienced and allowing them to respond quicker when problems or incidents occur. As organisations continue to grow in terms of their analytics driven capabilities, organisations will continue to leverage these capabilities to develop collaborative, adaptive teams.
Data Literacy Drives Successful Collaboration
While an organization can invest in any number of analytics tools, ultimately those tools will only provide benefit if used properly by individuals within that organization. To create a successful enterprise analytics strategy, it is critical to develop data literacy amongst all types of employees throughout an organization as they begin using analytics tools. Having the ability to correctly interpret metrics and fit them into larger organizational goals is essential for team success.
When business leaders work in conjunction with analysts using analytics tools, they increase their ability to ask better questions, refine their hypotheses, and develop recommendations that promote coordinated action. Developing this type of analytical culture allows the organization to share insights among teams; make collaborative decisions; and understand how different departments fit into an overall organization.
Encouraging data literacy among employees also allows leaders to encourage democratization. In doing so, leaders provide access to insights, challenge assumptions regarding the use of data, and allow stakeholders outside of a technical background to be involved in the creation of strategies based on data rather than intuition.
External Data and Open Metrics
Organizations that foster analytics collaboration invest in tools that support the sharing of data, visualization and workflow automation. Tools that streamlining collaboration amongst the enterprise support large numbers of external data source options, including from CRM and ERP platforms via modern Analytics as-a-Service platforms (AAAS). Many of these tools provide an integrated-view of data from various sources which reduces manual reconciliation work. Common tool features include:
- Self-service analytics dashboards
- Automated reporting process pipelines
- Shared workspaces for collaboration on queries
- Datasets accessed based on roles
By utilizing these features, teams operationally can filter through the insights they obtain rather than having all requests converge through central analytical teams.
Many organizations have begun to enrich their internal datasets with additional data that is both externally available (market indicators and benchmarks) as well as build a better understanding of the context in which they are making analytical decisions. Incorporating data sources into the decision-making process helps organizations identify additional criteria for measuring performance by comparing internal performance to that of its competitors in the industry. External data sources (e.g., news integrations) provide context to analytical output and change the way that teams view data and make strategic decisions.
Bringing Together Strategic Partners to Achieve Maximum Value Creation through Analytics
By leveraging specialized partners, businesses can increase the amount of value created from analytics while increasing the pace of adoption of analytics. For businesses developing a framework for developing and using analytics, including companies that provide support in tailoring analytics platforms to specific company requirements, the increase in adoption and strategic alignment supported by these organizations is accelerated.
For companies currently developing their capability to leverage analytical results to improve performance across different departments, it is advisable to partner with specialists who not only understand the technological aspects of analytics but the strategic realignment needed in the areas of people, processes, and culture. To look for trusted partners you can explore power bi consulting firms that can support companies in exploring their power bi consulting requirements to deliver optimum benefits through their analytical platforms.
In Conclusion
Analytics has grown from being a process to reporting results, to now serving as a foundation on which businesses run, make decisions, recover from bad decisions in “real time” and collaborate across departments. Through aligning their data practices, supporting open dialogue and sharing knowledge across all functions, organizations are able to convert partnerships with customers, suppliers and partners into a value-added competitive advantage. By developing a strategic analytics strategy, organizations improve the quality of their decisions and ultimately foster collaboration among diverse departments to create a unified goal.
