How to Create an Effective Enterprise Data Strategy

There is plenty of evidence pointing at data as a key ingredient of successful business. A recent study by Accenture shows that there is a correlation between being a high performer and becoming a data-driven enterprise. Another research project reveals that organizations that use data management tools are 58% more likely to surpass revenue targets than their less data-oriented counterparts. Yet another study indicates that data-driven organizations are 23 times more likely to acquire new customers and 19 times more likely to be profitable.

Then why are so many organizations still not using data to a full extent?

With the ampleness of data in the modern world, companies have a lot at their disposal if they want to improve operations and gain a competitive edge. However, with such a wide range of data and data management tools, organizations are paralyzed by the abundance of options and a lack of in-house expertise.

How can you resolve this?

One way to tackle the problem is to seek external data strategy consulting services. But before you start looking for a data partner, let’s see what enterprise data strategy is, how to approach it, and what role it plays in transforming traditional business into a highly efficient, data-driven enterprise.

What is an enterprise data strategy, and why do companies need it?

An enterprise data strategy is a comprehensive plan that outlines how a company manages, organizes, stores, analyzes, and otherwise uses its data to generate business value. It aligns data practices with business goals, ensuring data is accessible, secure, and actionable.

Our data consultants say that clients rarely come to us with an explicit request to establish an enterprise data strategy. Most of the time, businesses turn to us with other requests, such as building an AI system or overseeing their digital transformation. Even though data strategy is an indispensable part of such projects, clients don’t necessarily recognize it as such at first.

Why do companies need an enterprise data strategy?

Every data-intensive project relies on a well-thought-out enterprise data strategy. Working on such a project without having a formalized data strategy in place is akin to attempting to build the upper floors of a skyscraper without a solid foundation. Without proper support, the building will inevitably collapse.

For businesses, the absence of a data strategy can lead to missed opportunities, inefficiencies, and the risk of falling behind their competitors. A well-developed enterprise data strategy ensures that data is managed as a valuable asset, allowing businesses to harness its full potential for decision-making, innovation, and growth.

Take, for example, a retail company that seeks to automate their reporting processes using artificial intelligence. The company envisions using AI to generate insights faster and reduce manual workloads. However, they lack a comprehensive data strategy. Without a structured approach to how their data is collected, organized, and made accessible, implementing AI could be problematic. The company’s data is scattered across different departments and stored in inconsistent formats, making it difficult to integrate into an AI-driven system. Furthermore, the quality of the data may not meet the standards required for machine learning models, leading to inaccurate insights or failed initiatives.

By first establishing an enterprise data strategy, this retail company would not only lay the groundwork for successful AI implementation but also improve data quality, streamline access to critical data across departments, and ensure that the AI solution aligns with their business goals. This strategy would enable them to gain better insights, optimize inventory, and enhance customer experiences, ultimately giving them a competitive edge.

We recommend establishing or upgrading your existing enterprise data strategy during the following projects:

• Enterprise-wide digital transformation initiatives
• AI projects
• Data warehouse and business intelligence (BI) implementation
• Compliance and regulatory projects
• Mergers, acquisitions, and business expansion
• R&D and product innovation

Components of a successful enterprise data strategy

Let’s explore the key building blocks of a successful enterprise data plan.

1. Alignment with the company’s vision and business objectives

This component ensures that enterprise data strategy links back to the company’s goals and doesn’t operate in a vacuum.
If your data initiatives are not tied to concrete business outcomes, such as increasing revenue, enhancing customer satisfaction, or cutting costs, they risk being seen as a side project with limited business impact. Data activities will become fragmented, consuming resources without driving any real value. An effective enterprise data strategy will keep data projects focused on your most pressing objectives, ensuring that time and money are invested wisely.

Tips:

• Engage key business stakeholders early when building the strategy and systematically review your data initiatives to make sure they are in line with the evolving business goals.

2. Robust data governance framework

Data governance establishes company-wide rules on how to store, protect, and use data consistently and in compliance with the sector- and region-specific regulations.

This component is like the rulebook for handling and protecting data. Without it, inconsistent data management practices can lead to inaccuracies, privacy violations, or security breaches, causing reputational damage or hefty fines, especially in industries with strict regulatory requirements like healthcare and finance. When you have clear governance policies in place, everyone knows how to treat, share, and protect data, turning it into a trustworthy resource.

Tips:

• Invest in compliance tools and define and document acceptable data practices
• Appoint data owners, custodians, and users with clear accountabilities
• Emphasize the importance of metadata management to ensure all the data is documented and understood
• Automate data quality monitoring and compliance checks
• Check out our comprehensive guides on data governance best practices and how to build an effective data management strategy

3. Scalable data architecture and infrastructure

This component gives the technical foundation companies need to handle large amounts of data as their operations expand.

As your business grows, so does the amount of data you generate. If your infrastructure isn’t designed to scale, it might not be able to keep up with the volume and speed of incoming data. This can lead to slow processing times, an inability to access critical information in real time, or even system failures. By investing in a scalable data architecture from the start, you ensure that your business can grow without running into costly data bottlenecks.

Tips:

• Team up with cloud vendors and use their infrastructure for better flexibility and scalability. But be careful with transmitting sensitive data to the cloud. One solution is to use a combination of on-premises and cloud architecture to better protect sensitive information.

4. Seamless data integration and accessibility

With seamless integration, enterprise data flows freely across departments and systems. Every authorized staff member can access it in real time.

Isolating data in silos limits its usefulness. Teams can waste time trying to find, retrieve, or reconcile inconsistent data sets. Decision-makers may end up with incomplete information, leading to less informed decisions. Integrating your data across the organization ensures that everyone is working from a “single source of truth.”

Tips:

• Build a unified platform that aggregates data from different sources into one repository that everyone can access
• Implement APIs to facilitate data exchange between systems and applications

5. Data Quality Management

Data quality management focuses on maintaining accurate, complete, and consistent data that businesses can reliably use to make critical decisions.

Relying on poor-quality data is like trying to navigate with a broken compass – it leads you in the wrong direction. If businesses don’t systematically validate their data, errors will accumulate and the data will become inaccurate or incomplete, leading to costly consequences, such as poor product launches and inefficient marketing campaigns.

Tips:

• Implement automated data validation tools that can detect inaccuracies and clean data in real time
• Regularly audit your data to flag and resolve any quality issues that might have bypassed the automated checks

6. Advanced Analytics and AI

Artificial intelligence and its subtypes can analyze large amounts of structured and unstructured data and supply your employees with valuable insights. Machine learning can predict market trends and optimize inventory. Generative AI, which produces content that resembles human creation, has many business applications. It can generate documentation drafts, come up with new product ideas, interact with customers, and much more.

Without these tools, companies risk falling behind, as competitors who invest in AI can quickly identify trends, optimize operations, and tailor their offerings to customer preferences. By harnessing AI and predictive analytics, your company can uncover new opportunities and become more proactive in its strategies.

Tips:

Team up with a reliable AI development company and start with small, manageable projects that target your firm’s main pain points.

7. Data monetization

This component is responsible for turning your data into something that can either directly or indirectly generate revenue. For example, you can use customer data to personalize your offerings, increasing customer satisfaction and, consequently, sales.

Neglecting data monetization is leaving an untapped value on the table. You can use the data to improve your operations, or you can sell insights to other businesses. Either way, it’s worth exploring this additional revenue stream. Especially that data management costs can add up over time, and without monetization, the return on investment might remain unrealized.

Tips:

One suggestion is using AI to create premium, highly customized products. Another option is developing data-as-a-service offerings, where you can package your data (or external data that you aggregate and analyze) and sell it to other businesses.

8. Data security

Strong security practices keep sensitive data protected from unauthorized access, cyberattacks, and leaks.

Data breaches are becoming more common. Research shows that in 2023, attempts of unauthorized data access increased by 72% compared to 2021, with the average cost of one leaked data record amounting to $165. The implications of data leaks are not limited to financial losses. They can also lead to reputational damage and declining customer/employee trust.

Tips:

• Use encryption, data masking, and tokenization to increase security
• Implement role-based access and other security features to protect your data at rest and in transit
• Devise an effective recovery plan to come back from security incidents
• Conduct regular security audits and vulnerability assessments

9. Continuous Evaluation and Scalability

Establishing an enterprise data strategy is not a one-time project. It requires continuous reassessment to remain relevant and effective.
Technology evolves, business needs change, and what worked last year may not be sufficient today. Scalability is also crucial when it comes to data strategy. A rigid system can become a bottleneck as data volumes grow. Regular assessments and updates to your data strategy ensure that your systems can scale, your processes remain streamlined, and your business stays competitive in a rapidly changing market.

Tips:

• Stay informed about new data tools and industry trends
• Keep an eye on how the competitors are leveraging data and use this as a benchmark for your strategy
• Look inward and collect feedback from your employees to understand their emerging data needs
• Learn more about the topic in our software scalability guide

Recommendations to create a successful enterprise data strategy

There are ten recommendations that you can follow to craft an enterprise data strategy roadmap. These recommendations constitute the enterprise data strategy lifecycle, which covers four phases – discovery and alignment, integration and governance, enablement and execution, and optimization and measurement.

1. Connect your data strategy to business value. Make sure your enterprise data strategy corresponds to concrete business initiatives and produces business value. Data strategy is not just an abstract idea. It needs to be actionable, measurable, and specific. It has to tailor to your short-, mid-, and long-term business goals. Inform everyone about the business value your data strategy is supposed to deliver. This will help you avoid the situation where you have data tools that employees are forced to use without understanding the purpose.
2. Gather key stakeholders for a data discovery session. Bring together leaders from across the organization – IT, operations, marketing, finance, and beyond – for a focused data discovery session. This collaborative meeting will help everyone agree on the current challenges, opportunities, and key objectives that data can address. It will also enable you to secure executive support for your data initiatives from the start.
3. Audit your data ecosystem and identify data sources and flows. Conduct an in-depth audit of your existing data landscape. Assess how data is collected, stored, and analyzed. Flag gaps or inefficiencies and highlight opportunities for improvement. Pinpoint the most valuable data sources – both internal and external. Focus on collecting data that truly matters for business impact. By prioritizing high-value data, you ensure your teams have access to the most relevant and timely information.
4. Develop a data integration strategy. You need to aggregate and clean your data before you can use it in any company-wide initiatives. Break down silos and integrate data systems to create a unified view of information. Implement extract, transform, and load (ETL) tools, APIs, and middleware to synchronize data flows.
5. Prioritize data privacy and compliance. Embed privacy and security into your enterprise data strategy from day one. Implement strong data governance policies to safeguard your data and ensure compliance with the relevant regulatory frameworks, like the General Data Protection Regulation (GDPR) and Children’s Online Privacy Protection Act (CCPA). Closely monitor the evolving privacy regulations to protect customer trust and minimize risks.
6. Set up automated data quality checks. Implement automated rules that will continuously monitor data for anomalies, duplications, errors, and inconsistencies.
7. Empower your teams with the right tools. Equip your teams with powerful analytics and business intelligence platforms, allowing them to explore data, generate insights, and act on real-time information. Opt for tools that are easy to use for people without a technical background so that employees aren’t overwhelmed with overly technical solutions.
8. Establish a data-driven culture. Ignite a data-driven mindset across the company. Organize training sessions and ensure that every employee – from leadership to operations – understands the importance of using data to make informed decisions.
9. Commit to continuous optimization and innovation. Review and adjust your enterprise data strategy to incorporate emerging technologies, additional data sources, and changing market conditions.
10. Create reliable data performance metrics. Define clear, measurable KPIs to track the effectiveness of your enterprise data strategy. Regularly evaluate performance against these metrics to ensure your data initiatives are generating the intended business outcomes.

ITRex as your trusted data partner

Our approach is to look at the business side first, understand your challenges, and come up with the best-suited framework/technology from the perspective of costs, value, risks, and implementation speed.

By partnering with us, you’ll get a data strategy that’s adaptable, future-proof, and designed to deliver tangible business outcomes – whether it’s streamlining operations, enhancing decision-making, or unlocking new revenue opportunities.

Projects from our portfolio

To demonstrate our expertise, we want to share with you three exciting enterprise data strategy examples from our portfolio.

We will measure the impact of the new data strategy over four criteria:

1. Processes. How did the existing processes change? Which new processes were implemented?
2. Technology. Did our intervention result in deploying new technology?
3. People/culture. How did employees’ attitudes change?
4. Policies. Did the new enterprise data strategy affect the existing policies?

Optimizing raw material delivery

The client delivers raw materials to construction companies across the US. With a fleet of trucks operating around the clock, numerous factors can disrupt timely deliveries – ranging from vehicle breakdowns and traffic congestion to bad weather. To address these challenges, the client wanted to undergo digital transformation, which would enable them to switch from manual delivery monitoring to automated monitoring using smart sensors, predictive analytics, and recommendation systems.

Ultimately, they wanted to aggregate and access more data, analyze it in real time, and adopt a more proactive business strategy.

Our contribution

The client saw the need to rethink their enterprise data strategy, prompting our data consultants to organize a workshop with the key stakeholders. This collaborative effort helped us assess the current landscape, including existing data sources and the company’s decision-making processes. Together, we outlined the client’s future aspirations and developed a strategic roadmap to achieve these goals.

As part of the revamped data strategy, our team designed and implemented a cloud-based enterprise data platform that integrates data from diverse sources, enabling seamless access. This platform leverages selected cloud services, interconnected to aggregate and analyze data, providing valuable insights to every employee involved in the delivery chain. We also devised data quality management practices to make sure all the incoming data is consistent and clean.

Our team helped the client to install IoT sensors on their trucks, integrate a predictive analytics model to forecast traffic and weather conditions, and implement a recommendation system that can suggest alternative vehicles to take over the carriage and optimize delivery routes. All these tools rely on the aforementioned data platform.

Impact

Facilitating drug discovery and streamlining operations for a pharma company

A prominent pharmaceutical company approached ITRex to review and reorganize their data management practices to be ready to support new company-wide innovative business initiatives and goals, such as:

• Facilitating collaboration among researchers while maintaining security and compliance
• Improving clinical trial data management, including the data streams coming from personal wearable devices
• Implementing artificial intelligence to streamline research and drug discovery and some internal processes like inventory management

We proposed to devise a new enterprise data strategy that would focus on cloud adoption, better data governance, improved collaboration and data sharing, strict data security and privacy, and the introduction of AI for data analysis and decision-making. The client agreed.

Our contribution

Our data consultants conducted a series of workshops with the client to gain a deeper understanding of their business needs.

As the company wanted to process enormous volumes of structured and unstructured data, including data streams from patients’ IoT devices, we had to ensure adequate storage and prescribe compliant data practices to increase efficiency and security of the incoming data. All this data, along with other data sources, would be used to fuel the AI models specialized in drug discovery. We also needed to ensure access to external data sources for AI models that analyze the stock and pricing models and refill the inventory accordingly.

After understanding the client’s data landscape, we began crafting a detailed roadmap. We established data governance roles and data stewardship practices, ensuring transparency in data usage and proper data protection and audit. Our team implemented a dedicated AI algorithm to help monitor the incoming data, improve data observability, and alert responsible data owners to any identified inconsistencies or unexpected data load behavior. To support safe collaboration among researchers within and outside the company, we ensured secure data transit, as well as anonymization and de-identification of personally identifiable information (PII) used in clinical research.

In accordance with the new enterprise data strategy, we improved the client’s research platform by integrating a range of cloud data-related services. Operating within a private cloud environment, the platform featured strict access control policies. It aggregated clinical trial data and other information, fed it to the AI models, and presented the output in a predefined format. Researchers could also use this platform to collaborate securely with outside consultants.

Impact

Optimizing fresh produce logistics

The client operates in the food supply chain domain. They manufacture reusable smart trays for storing and transporting fresh produce, such as fish, meat, fruits, and vegetables.

Previously, the client’s logistics process involved simply loading the produce into the smart trays and delivering them to the final destination without considering critical factors, such as expiration dates and delays. This inefficiency led to spoilage and subsequent financial losses. Farmers and other produce owners couldn’t track their harvest across the supply chain, which reduced customer satisfaction.

Another problem was that employees couldn’t access the relevant data in real time. They had to preorder business intelligence reports that would arrive with unacceptable delays when this data is no longer relevant.

The client wanted to enhance data visibility and transform the way employees access data. They turned to ITRex to craft a corresponding enterprise data strategy that would enable them to seamlessly integrate and use data from different sources, such as demand trends, production, data incoming from smart trays, transportation vehicles, etc.

Our contribution

We analyzed the specifics of the client’s business and put forward a robust plan that would enable them to harness data to generate timely, actionable business insights. Our team also established a data governance framework to ensure consistent data management across the organization.

As a part of the new enterprise data strategy and to resolve the problem of delays with business reporting, we created a company-wide operational data hub that acted as a self-service BI platform. Authorized personnel used this hub to access data and build reports ad hoc without submitting prior requests.

In addition to internal company data, this hub aggregated real-time IoT data from smart trays to track produce freshness and transportation conditions. This data became available for analysis and provided actionable insights into various aspects of produce logistics, helping stakeholders make informed decisions.

Our team also developed a set of reports for farmers and external users, allowing them to track their produce as it moves through the supply chain. We enabled this external access via a secure and audited web portal.

Impact

References from our blog

In addition to the references included throughout this article, you can visit our blog for more information on data-related topics:

• What is data migration?
• Guide to automated data collection
• Costs associated with data analytics
• Top big data challenges
• What is self-service BI?
• Data warehouse vs. data lake vs. data lakehouse
• Big data and its business impact
• What is data mapping?
• Synthetic data generation with Gen AI

Our data analysts also prepared three comprehensive guides on data in the healthcare field.

• Big data in healthcare
• Healthcare data interoperability
• Healthcare data warehouse

Why work with ITRex?

ITRex offers more than dedicated data consultants. We are a diverse team that can address your needs from different angles. We will not only put forward a comprehensive enterprise data strategy but will also help you with implementing the right tools.

Our data expertise includes:

• Data consulting, such as data strategy development
• Data engineering, which includes designing and building cloud-based and/or hybrid data platforms, such as data warehouses, data lakes, and data lakehouses
• Ensuring secure data transit and storage
• AI/ML product development, business intelligence reporting, and data preparation for machine learning models
• And more

In addition to data, we have vast experience in software development and cutting-edge technologies, including artificial intelligence consulting, advanced analytics, the Internet of Things (IoT), augmented reality, and more. Our developers will implement innovative solutions, finding creative ways to put your data strategy into action.

Our experts have worked in healthcare, retail, media and entertainment, and other sectors. We understand the unique challenges each industry faces. This allows us to provide tailored insights and solutions. We develop data strategies with strict attention to data governance, security, and compliance requirements, ensuring your organization’s data remains safe and adheres to industry regulations.

So, get in touch and use this expertise to your advantage!

Work with our consultants to transform your data into actionable insights that drive business growth. We will help you unlock the full potential of your data for long-term success!

 

Originally published at https://itrexgroup.com on October 30, 2024.

The post How to Create an Effective Enterprise Data Strategy appeared first on Datafloq.

Source link