Big Data strategy: How to achieve data-driven success

According to Gartner, over 80% of enterprise data is unstructured, originating from diverse sources such as emails, IT logs, customer service interactions, and business documents. Without a strategic approach, organizations often find themselves drowning in data with no clear way to use it effectively. These factors can lead to fragmented workflows, poor data quality, and isolated decisions-problems that can ultimately negatively impact your business.

This is the point where a well-defined Big Data strategy comes into play. A strategy turns your data into actionable insights to help you predict trends, understand your customers better, streamline operations, and create new products and services. With a well-defined strategy, data can become one of your greatest assets. In this guide, we will outline the principal elements and challenges of building a successful strategy.

Defining Big Data strategy

A Big Data strategy is a comprehensive plan that outlines how an organization will manage, store, analyze, and use large volumes of data to achieve its goals. All of this involves converting complex datasets-such as transaction records, social media interactions, sensor data, and website logs-into valuable information.

What differentiates data management from data strategy? Data management concentrates on the technical aspects of data handling, storage, processing, and security. It is about ensuring that data is available, accurate, and secure. While these are crucial elements, they are only part of the equation. A data strategy, on the other hand, takes a holistic view. It considers how data drives business outcomes and how it can be integrated into the overall business strategy.

What are the goals of Big Data strategy consulting?

• Consolidate all data sources into a unified data platform, enabling seamless access and reducing data silos.
• Develop capabilities to process and analyze data in real-time, allowing immediate insights and quicker decision-making.
• Utilize advanced analytics and Machine Learning to forecast trends and behaviors, supporting proactive business strategies and risk mitigation.
• Use data analytics to create detailed customer segments based on behavior, preferences, and demographics to drive targeted marketing and personalized experiences.
• Streamline data collection, cleansing, and integration processes through automation to improve accuracy and reduce manual workloads.
• Establish robust security measures, such as encryption and access controls, to safeguard sensitive data and comply with regulations.
• Leverage data insights to identify customer needs and gaps in the market, guiding the development of new products or enhancements to existing offerings.
• Analyze data to identify cost-saving opportunities across supply chains, production, and service delivery.

Let's not forget: Organizations should adopt multiple Big Data strategies, such as combining customer analytics with operational data insights, to create a more holistic view of their business. It must support data-driven decision-making at every level, from front-line operations to C-suite strategy. To do so means moving beyond isolated data projects or ad-hoc analytics and building a cohesive, enterprise-wide approach that ensures data is accessible and actionable.

Key components of a Big Data strategy

A proven big data strategy roadmap encompasses several core elements, each of which plays a role in transforming raw data into actionable insights. Here, we explore the fundamental components of a robust strategy and provide real-world examples from the N-iX experience to illustrate their implementation.

Data collection and integration

For enterprises, data doesn't just come from one place-it's harvested from many sources. These range from transactional data in traditional databases to less structured forms such as social media interactions, sensor readings from IoT devices, customer feedback, and even third-party data feeds. The variety of data types presents both an opportunity and a challenge.

The primary goal of data collection is to capture as much relevant information as possible to fuel analytics and decision-making processes. However, the real value of data comes from integration. Data integration should set up a seamless data ecosystem where information flows freely across departments and systems without losing context or meaning. Such a scenario requires robust data pipelines that can handle diverse data formats and transform them into a standardized structure that can be easily analyzed and interpreted.

To achieve effective integration, enterprises often employ:

• ETL (Extract, Transform, Load) tools help extract data from other sources into a consistent format and load it into a centralized repository like a data lake.
• Data lakes or cloud-based solutions, in particular, offer a flexible, scalable solution for storing vast amounts of structured and unstructured data.

Implementing Big Data strategy from N-iX experience: We partnered with a global enterprise to manage a complex data integration project consolidating structured and unstructured data from over 100 sources. The project involved integrating data from various customer touchpoints, operational systems, and external data sources.

We created a unified data platform with a 360-degree view of the business. After the deployment, the client could identify trends, optimize operations, and enhance customer experiences based on data collection and analytics. N-iX developed a scalable, cloud-based data integration architecture that leveraged advanced ETL processes, real-time data streaming, and Machine Learning algorithms for data enrichment.

Read more: Scalable big data analytics platform for leading industrial supply company

Data governance and security

A practical strategy incorporates a comprehensive data governance framework that aligns with internal policies and external regulations like GDPR. This framework is a lot more than just about compliance; it's about building trust-with clients, partners, and stakeholders.

Effective data governance involves defining clear data ownership and stewardship roles within the organization, setting standards for data quality, and establishing processes for data lifecycle management. It's about ensuring that the right people have access to the right data at the right time.

On the security side, deploying robust measures that protect data at rest and in transit is essential. The implementation of data governance includes setting up encryption and multi-factor authentication and conducting ongoing security audits. Additionally, modern strategies increasingly look at advanced solutions such as AI-driven threat detection and detection systems. These can identify potential breaches in real-time and take immediate action to mitigate them.

Implementing from N-iX experience: Our team developed a comprehensive data security strategy, focusing on security measures, access control, and compliance with data privacy regulations. We implemented advanced encryption algorithms for data at rest and in transit, set up a robust RBAC system with MFA, and conducted regular penetration testing and security audits. Beyond that, N-iX employed data masking techniques to protect sensitive customer information during the software development and testing phases. This multi-layered security approach protects data against unauthorized access and is aligned with requirements.

Read more: Driving growth in ecommerce with a comprehensive data analytics solution

Data analytics and insights

At the core of a strategy is the ability to convert raw data into actionable insights. Data analytics extends to using advanced analytical techniques and tools to explore data, identify trends, and generate insights.

For enterprises, advanced analytics capabilities encompassing descriptive, diagnostic, predictive, and prescriptive analytics can transform how decisions are made and strategies are formed. Data analytics can range from

1. Descriptive analytics provides a rear-view mirror look at historical data, helping organizations understand what has happened in their business.
2. Diagnostic analytics goes a step further and identifies the causes of those outcomes.
3. Predictive analytics, powered by Machine Learning models, allows for forecasting future trends and behavior patterns based on historical data.
4. Prescriptive analytics suggests actions that could shape future outcomes, providing a proactive rather than reactive approach to business strategy.

Moreover, leveraging advanced tools and platforms that support Big Data analytics, such as Apache Hadoop, Spark, and cloud-based analytics services, significantly enhances your ability to process large datasets quickly and efficiently. Data analytics is not simply a one-time effort but an ongoing process. It involves constantly refining algorithms, updating models, and exploring new ways to leverage data for business advantage.

Implementing from N-iX experience: We partnered with a financial services provider to implement a Machine Learning model for accurately predicting customer churn. By integrating this model into their CRM system, the client could proactively engage at-risk customers, reducing churn and increasing customer retention.

Read more: Driving growth in ecommerce with a comprehensive data analytics solution

Data architecture and infrastructure

As organizations generate vast amounts of data from various sources, it is necessary to have a well-structured architecture that can efficiently handle the volume, velocity, and variety of data. It should be capable of scaling horizontally and vertically to handle massive data sets while supporting diverse data types, from structured and semi-structured to unstructured data.

When designing a future-proof architecture, we consider the following components:

• Data lakes and warehouses are foundational elements storing large amounts of raw data and processed information. Data lakes are repositories for unstructured and semi-structured data, while data warehouses are optimized for structured data and analytics.
• Data pipelines: Efficient data pipelines are essential for moving data from various sources to storage systems and processing units. Data experts design it to handle batch and real-time processing, ensuring data is available when needed.
• Scalable storage and compute resources: The architecture should support scalable storage solutions, like cloud storage, that can grow with the business. Similarly, computing resources should be flexible enough to handle varying workloads, leveraging containerization and serverless computing technologies.

A modern data infrastructure often relies on a combination of on-premises systems, cloud-based platforms, and hybrid solutions.

1. On-premises infrastructures offer control and customization, making them suitable for organizations with specific security or regulatory requirements.
2. Cloud-based platforms provide scalability, flexibility, and cost-effectiveness, making them ideal for handling large-scale data analytics and storage needs.
3. A hybrid approach merges the best of both systems, allowing businesses to keep sensitive data on-premises while leveraging the cloud for scalable analytics and storage.

Implementing from N-iX experience: We recently teamed up with a leading global retailer to develop a hybrid solution to securely store and process huge amounts of customer and transaction data. N-iX designed a hybrid architecture that integrated on-premises servers for sensitive data with a cloud-based data lake for analytics and Machine Learning.

Read more: Facilitating shopping experience and increasing sales for a luxury store chain

Challenges of Big Data strategy implementation

Handling the volume, variety, and velocity of data

The high volume, variety, and velocity of data generated could present significant challenges for organizations. Managing large datasets, handling diverse data formats, and processing data in real time requires advanced technologies and infrastructure. Without the right resources, tools, and expertise, businesses may struggle to harness the full potential of their data.

Our recommendation: N-iX design and implement scalable data architectures to accommodate growing data volumes and diverse data types. The company leverages technologies like distributed computing, parallel processing, and cloud-based solutions to handle high-velocity data streams.

Data quality

Data from different sources often comes in various formats and can be incomplete, outdated, or contain errors. This lack of standardization makes integrating and analyzing data effectively difficult. Poor data quality reduces analytics accuracy and increases the time and resources needed to clean and prepare data for use.

Our recommendation: We implement robust data quality frameworks that include automated data cleansing and validation processes. With advanced ETL tools and machine learning algorithms, N-iX ensures that data is consistently formatted, accurate, and up-to-date across all sources.

Scalability and performance

Another significant challenge is the scalability and performance of data infrastructure. Traditional systems may struggle to process and analyze large datasets efficiently. Enterprises can face slow query responses, bottlenecks, and increased costs. Moreover, handling real-time data processing for applications like predictive analytics and customer personalization can further strain the infrastructure.

Our recommendation: Using distributed computing frameworks such as Apache Hadoop and Spark, N-iX enables parallel processing of large datasets. We employ cloud services from leading providers like AWS, Azure, and Google Cloud to guarantee that your data infrastructure can quickly scale up or down based on the need.

Change management

Introducing a data strategy requires significant changes to an organization's processes, technologies, and mindsets. Achieving buy-in from all stakeholders, from C-level executives to frontline employees, can be challenging.

Our recommendation: We facilitate clear communication of the strategic vision, benefits, and expected outcomes of the significant data initiative, as well as provide training and support for smooth big data implementation strategy and its execution.

Integration of disparate data sources

Big Data strategies require consolidating information from various systems such as CRM, ERP, social media, and IoT devices. These systems often have different data formats, schemas, and protocols. As a result, it makes it challenging to create a unified data view. Without proper integration, valuable insights can be lost, and data silos may persist.

Our recommendation: N-iX creates seamless data integration solutions using advanced ETL processes, API management, data virtualization techniques, and middleware technologies. We simplify real-time integration from multiple sources without dramatically changing existing systems.

Read more: How to drive maximum business value from big data development

Final thoughts

Getting to the big data strategy implementation isn't easy. It means overcoming challenges like integrating data from different sources, ensuring it is accurate and secure, and choosing the right technologies to manage and analyze your data. It also calls for building a culture where data-driven decision-making is the norm, not the exception. Doing this results in smarter, faster, and more strategic decisions.

This journey demands the right technology and partner-one who understands the complexities and challenges of Big Data and can guide you through its implementation and optimization.

Now is the time to take the next step. Over 21 years, we offer a range of services starting with designing and implementing ETL, DWH, or OLAP, building data lakes or data lake houses, establishing data visualization and automated reporting, and deploying sophisticated analytics models. N-iX has delivered over 60 data projects and has more than 200 data and 400 cloud experts.

We have successfully delivered data analytics projects across various industries, including telecom, manufacturing, healthcare, and more. Our experts maintain and protect data by complying with all established data security standards, including GDPR, ISO 27001:2013, ISO 9001:2015, and PCI DSS.

Let's explore how we can partner together to build a future where your data works harder for you.