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Making sound business decisions is easier when your organization has only 50-70 employees and a few projects. The small staff means you may keep in good contact with everyone, and staying up to date doesn’t take much effort. A small project portfolio means you have total control over what happens on each project.
This changes as the company grows. More data is needed to make informed decisions, and this information is often scattered among several separate systems. HR has a silo, IT has a silo, marketing has a silo, PMO has a silo, etc.
To achieve the hallowed goal of data-driven decision-making at scale, you wish to build a data ecosystem that breaks down these silos. Centralizing and standardizing data from all these sources provides a holistic view that streamlines data evaluation.
This omniscience gives you the ability to make decisions supported by concrete evidence, reducing risk and leading to more strategic and accurate selections.
The value of a data ecosystem
If you are hesitant to commit to building a data repository, that is comprehensible. This will be a significant investment of time, resources and budget.
You know there is value in it, but you could not realize the full extent of it. From my perspective, these are the basic advantages of building a data ecosystem that directly helps build a solid organization:
- Better customer understanding: The data ecosystem allows you to collect and analyze customer data from various touchpoints. This data enables a deeper understanding of customer behavior, preferences and needs. You can then use this information to personalize your marketing campaigns, develop targeted products and services, and improve customer satisfaction.
- Increased efficiency and productivity: AI-powered data infrastructure streamlines processes by automating tasks and eliminating manual data collection and evaluation. Employees who previously dealt with these tasks can freely focus on higher-level activities.
- Innovation and development: Data repositories support innovation by providing a platform for collaboration and knowledge sharing. By accessing diverse data sets and insights from peers, you may discover recent opportunities, develop data-driven products and services, and increase your organization’s competitive advantage.
- Risk management: Improved data evaluation helps you proactively discover and mitigate risk by providing a broader view of market trends, customer sentiment and potential disruptions. This gives you an advantage in managing risk and developing contingency plans.
To build a scalable data ecosystem that delivers these advantages, focus on the following five key areas.
1. Centralize data for accessibility
When you build a puzzle, you first place all the pieces on the table and turn them face up. You can then start analyzing them to find the edges, fit the pieces together, and complete the puzzle to see what picture it creates.
You don’t put all the edges in one cup, the blue ones in one other, the red ones in the third, and so on. Why would you? This would make it difficult to see how the pieces come together.
Likewise, storing data in silos makes it inconceivable to see connections. Centralizing data increases its accessibility to stakeholders, reduces data silos, and enables consistent data evaluation across the organization.
To facilitate this level of end-to-end data access, collect and integrate data from various business operations (equivalent to HR, project management, and code repositories) into a data lake. You can use services like AWS, Google Cloud, or Azure to create robust, secure, and scalable data lakes.
2. Use technology to integrate data
Once you have a centralized data repository, you wish a scalable process to keep your data current and accurate. You’ll quickly discover that the manual processes you have been using aren’t up to the task.
As more and more data flows into your business, automation becomes crucial. APIs and webhooks can automate the data ingestion and integration process by pulling data from various systems into a data lake.
3. Use advanced analytical tools
Visualizing and analyzing this wave of information requires advanced business intelligence (BI) and analytics tools like Tableau, Power BI, or custom dashboards developed on platforms like Shiny, Apache Superset, or Dash.
These tools generate real-time insights and forecasts that support strategic decision-making. This may include analyzing project schedules, workforce productivity, or software development cycles.
4. Foster a data-driven culture
After implementing technology, the next step is to create a data-driven culture in the organization. Invest in training programs to increase worker data literacy. Data literacy enables team members to understand and effectively use data in on a regular basis decision-making processes.
Take steps to ensure universal access to data and analytics across your organization. This integration encourages data-driven decision-making at all levels.
5. Maintain data governance and compliance
Many laws govern the kinds of data that will be collected, how they will be used, and how they will be protected. Failure to comply with these regulations may result in high financial penalties, not to mention the costs of mitigating the damage in the event of a data breach.
Develop and implement a data management policy to protect your business and maintain data integrity and security. This policy describes how your organization uses and manages its data, who can access the data and how it could be used. It also establishes policies to help protect data, defines the roles and responsibilities of information management officers, and establishes data security and quality standards.
Data management professionals should usually review legal and regulatory requirements related to data privacy and security, especially across geographic jurisdictions. Regulations change and there are a lot of them to sustain with.
Business, know yourself
You often hear entrepreneurs say, “We don’t know what we don’t know” when talking about data collection gaps. But when data is in silos, it’s more like saying, “We don’t know what we know.” The information is there, but you simply cannot put all the pieces of the puzzle on the table.
However, focusing on these strategic areas can make it easier to build an effective data ecosystem that can make it easier to make informed decisions as your business scales. This leads to improved operational efficiency, better strategic planning and a competitive advantage in the market.