IT Foundation: Big Data/ Analytics and Cybersecurity

Big Data and Analytics

Companies like Amazon, Spotify, and online fashion retailers have mastered the art of personalized recommendations, seamlessly anticipating and meeting customer preferences with uncanny accuracy.

But what lies beneath this seemingly magical feat of insight and foresight?

Online platforms meticulously collect a wealth of data about user behavior, spanning from purchase history and browsing activity to device usage patterns and time of engagement. This trove of information serves as the raw material for advanced analytics algorithms, which delve deep into the data to uncover hidden patterns and trends. For instance, Amazon's recommendation engine employs sophisticated algorithms that analyze user browsing and purchase history to identify patterns and similarities among users with comparable preferences. This enables the platform to generate personalized recommendations tailored to each individual's tastes and preferences, leading to higher customer satisfaction and increased sales.

The power of big data and advanced analytics extends far beyond e-commerce, permeating industries ranging from manufacturing to healthcare. For instance, a leading automotive manufacturer utilizes advanced analytics to optimize production processes and enhance supply chain efficiency. By analyzing vast amounts of production data in real-time, the company can identify bottlenecks, predict equipment failures, and optimize resource allocation, ultimately improving productivity and reducing downtime.

Moreover, big data and advanced analytics play a pivotal role in shaping strategic decision-making across organizations. Take the example of a global telecommunications company seeking to enhance customer retention and reduce churn. By leveraging predictive analytics models, the company can analyze customer data to identify early warning signs of dissatisfaction and proactively intervene with targeted retention strategies. This proactive approach not only strengthens customer relationships but also drives long-term loyalty and profitability.

However, the journey towards becoming a data-driven organization is not without its challenges. Companies must navigate complex technical landscapes, organizational silos, and talent shortages to unlock the full potential of big data and advanced analytics. Investing in robust data infrastructure, fostering a culture of data-driven decision-making, and cultivating cross-functional collaboration are critical steps towards realizing this vision.

3 CSFs (Critical Success Factors)

1.Vision and Objective Setting: Successful transformation into a data-driven company begins with a clear vision. This involves outlining a comprehensive business case with specific, measurable goals for various applications, such as enhancing customer retention or optimizing production. The vision should also include a detailed transformation roadmap, breaking down the journey into stages with set deadlines.

2.Targeted Application of Data Analysis: Companies need to identify specific business process areas where data analysis can be applied effectively. This could involve exploring new domains like smart homes or digital health, or improving traditional areas such as customer engagement, direct communication, cross-selling, machine utilization, predictive maintenance, and employee retention.

3.Strategic Implementation with Quick Wins: The big data strategy should commence with a manageable yet complex application that promises significant returns. Achieving quick results in these initial projects can provide momentum for the broader transformation effort. As multiple subprojects begin to yield rapid benefits, they can collectively finance the ongoing transformation process.

Foundational Blocks

1.Data as the New Gold:

• Data is central to the digital economy, akin to gold.
• Example: Google has shown what is possible with large-scale data collection.
• Challenges include technical and organizational barriers in integrating and analyzing data across different systems and departments.

2.Importance of Analytics:

• After collecting data, the focus shifts to analysis, encompassing descriptive, predictive, and prescriptive analytics.
• These analytical approaches explain past events, predict future trends, and recommend future actions.
• Advances in machine learning and AI open up new opportunities for analysis.
• Companies like Amazon and Google offer their algorithms publicly, but keep their data private, indicating the value they place on their data.

3.Tools for Data Analysts and Users:

• Professional software like SAS or IBM Modeler assists data analysts in managing and structuring data.
• Open-source software like R or Python is increasingly popular due to rapid development and advanced algorithms.
• Visualization tools like Tableau help present analysis results in an easily digestible format.

4. Role of Translators:

• Translators bridge the gap between technical talent (data scientists and engineers) and decision-makers in management.
• They require a deep understanding of both the digital and business worlds.
• The importance of expertise in employees for utilizing big data and analytics is often underestimated by companies.

5. Processes Uniting All Elements:

• The effectiveness of data, analytics, tools, and employees depends on robust processes.
• Poor quality data or flawed processes can render even the best analytics ineffective.
• An example from consulting illustrates how big data and advanced analytics, when applied correctly, can solve complex problems and drive business growth.

A sample big data & analytics module list

This gives a sample vision, strategy, Cloud and IoT, applications, commercial levers, internal optimization, new operating model and foundational blocks.

Vision

- Development of a vision: Establishing a long-term goal or set of goals for big data and analytics within an organization.

- Vision and target: Defining specific targets that align with the broader vision for the analytics initiatives.

- Foundation diagnostics: Assessing the current state of an organization's analytics capabilities and infrastructure as a baseline for improvement.

- Prioritization of applications and road map: Identifying key analytics applications for the business and planning their development over time.

Strategy

- Data as an asset: Recognizing and treating data as a valuable resource that can drive business growth and efficiency.

- Social media listening: Using analytics to monitor and analyze data from social media platforms to gain insights into customer behavior and market trends.

- Agile test-and-learn pilots: Implementing small-scale experiments quickly to test hypotheses and learn from the outcomes, enhancing agility in decision-making.

Cloud and IoT

- Cloud analytics: Leveraging cloud computing resources to perform big data analytics, which can be more scalable and cost-effective.

- Internet of Things (IoT): Connecting devices to the internet to collect and exchange data, which can then be analyzed for insights.

- Crowdsourcing of globally available know-how: Gathering information and insights from a large, dispersed group of people, typically via the internet.

Insights and AI

- Key insights in real time: Utilizing analytics to gain immediate understanding and actionable information from data as it's collected.

- Artificial intelligence: Applying AI techniques, such as machine learning, to analyze data and make predictions or decisions without human intervention.

- Automation and robotics: Using technology to perform tasks without human assistance, often informed by data analytics.

Applications

- Commercial levers (sales-specific): Applying analytics to drive sales through various strategies like assortment optimization, cross/up-selling, etc.

- Internal optimization (capex/opex): Using data to optimize capital expenditures (capex) and operational expenditures (opex) for better internal resource management.

Commercial Levers

- Assortment optimization: Analyzing data to determine the best mix of products to offer.

- Dynamic B2C pricing: Adjusting consumer prices in real-time based on analytics.

- Value-based B2B pricing: Setting prices for business clients based on the perceived value of products or services.

- Customer migration: Analyzing data to understand and influence customer transitions between product tiers or services.

- Next-best action/offer: Using predictive analytics to determine the most effective action or offer for a customer at any given moment.

- Acquisition: Leveraging data to identify and acquire new customers or assets.

Internal Optimization

- Predictive maintenance: Using data analytics to predict when maintenance should be performed on equipment.

- Demand forecast: Predicting future customer demand for products or services using historical data.

- Debt management: Applying analytics to manage and collect debts efficiently.

- Fraud/theft identification: Detecting fraudulent activities or theft through analysis of transactional data.

- Forecast of risk/debt losses: Estimating potential losses due to credit risk or bad debts using statistical methods.

Foundation

- Data: The actual data collected from various sources that will be used for analytics.

- Analytics: The techniques and processes of analyzing raw data to make conclusions about that information.

- IT: Information technology infrastructure that supports the collection, storage, processing, and analysis of data.

- Employees: The human resources who manage and execute data and analytics processes.

New Operational Model

- Culture and attitudes: The organizational culture and mindset that supports data-driven decision-making.

- Performance management: Systems and processes to track and manage the performance of data analytics initiatives

- Processes and steering: The governance mechanisms for managing and directing analytics operations.

- Organization: The structural aspect of how the analytics function is integrated within the wider organization.

Cybersecurity

In recent years, the digital landscape has witnessed several high-profile cyber attacks that have underscored the vulnerabilities inherent in our interconnected world. From the notorious Bangladesh central bank heist to the Sony Pictures breach and the Volkswagen car key code theft, these incidents serve as stark reminders of the risks posed by cyber threats. With the global economy becoming increasingly digitized, the potential for cyber attacks looms large, with projected losses reaching billions of dollars by 2024.

The Varied Faces of Cyber Threats

Cyber attacks emanate from diverse sources, each with distinct motives and objectives. Nation-states may engage in cyber espionage to gain economic or political advantage, while competitors might target rival technologies for illicit gains or reputational damage. Ideological groups, known as hacktivists, pursue agendas ranging from exposing corporate injustices to promoting specific ideologies. Furthermore, insider threats, whether through disgruntled employees or compromised individuals, add another layer of complexity to cybersecurity challenges.

Steps towards Cyber Resilience

To counter the evolving threat landscape, organizations must embrace a proactive approach to cybersecurity, focused on building cyber resilience. Here are seven key practices that can bolster cyber resilience:

1. Prioritize Data Inventories and Business Risks: Assess and prioritize critical business data to identify potential risks and vulnerabilities. 4Very few companies have a clear idea of which business data is the most important. Security teams therefore need to work with management in the first step to examine the entire value chain and assess where the greatest risks lie. Is it the data for the design of a new product, a self-learning manu- facturing process, or sensitive customer data, the loss of which would lead to a maximum credible accident?

Use Case: Banks and insurers adopt a "crown jewels" program to safeguard their most sensitive data, serving as a model for other industries.

2. Mobilize Frontline Employees: Educate employees on the value of data and involve them in cybersecurity efforts.

Use Case: Microsoft's founder, Bill Gates, prioritizes product security and implements continuous patching initiatives since 2002.

3. Integrate Resistance into Processes: Embed cybersecurity measures into cross-company processes to fortify organizational resilience.

Use Case: General Electric (GE) integrates security components into the design of its machines and software, emphasizing security as a core element.

4. Implement Incident Response Mechanisms: Establish incident response mechanisms across all business functions and conduct realistic testing to refine response strategies.

Use Case: United Airlines and Barclays Bank engage in bug bounty initiatives and internal hacking exercises to identify and remediate vulnerabilities proactively.

5. Integrate Security Functions in Technology: Infuse security elements into IT architecture components and prioritize security in technology governance.

Use Case: GE elevates IT security to the board level and conducts regular security audits to safeguard critical infrastructure.

6. Differentiate Protection Levels: Tailor security measures based on the sensitivity and importance of data assets, employing varying levels of encryption and access controls.

Use Case: Banks implement enhanced security measures for high-value transactions, utilizing multi-factor authentication for added protection.

7. Deploy Active Protection Systems: Deploy active defense mechanisms to enable real-time response to cyber threats and enhance overall security posture.

Use Case: Lockheed Martin initiates the Nexgen alliance for early threat detection, bolstered by EMC's acquisition of NetWitness for advanced threat analysis.

Integrate Security Technology as part of IT architecture

Operating systems, communication logs, and applications are established elements of the IT architecture. Each of them can become a security risk if it offers access points for possible attacks because of poor configuration, testing, and maintenance. Security elements must be built into all compo- nents, whether hardware, middleware, or application software, and their resilience must be continuously tested and refined during the development process. Together, all of these components mean a huge volume of poten- tial security problems that can be identified only through persistent testing and ongoing maintenance.

With the trend toward rapid digitization, many companies in recent times have hastily introduced new technologies for which they lack the necessary administrative skills, and are unable to understand the technologies’ interaction with the existing architecture. Budgets are reallocated from the maintenance of the old IT systems to the establishment of new digital skills, which makes sense from a business perspective but often has serious consequences for the medium-term security of the IT architecture.

One important step is to introduce different security zones. A European sports apparel manufacturer, for example, introduced a “play zone” in which online campaigns can quickly be created and implemented. The play zone is isolated from the existing systems with its own security zone. This means if security problems occur, the campaign can be quickly stopped or even deleted without the other systems being affected.

Another key element is ownership of the security elements. Often, the enemy is internal, with responsibility disputes among IT, the secu- rity organization, and product development tying up resources and budgets. Security technology, however, should be given top priority in the governance of each company. For example, GE firmly anchored an IT security function on its board of directors. Business units and the firm’s head office are regularly and systematically subjected to security audits. According to Bill Ruh, head of the GE software division: “At GE, we focus on software platform security, protect critical infrastructure elements, and help our customers to perform reliable and secure trans- actions online.”

To Sum up:

• As foundational blocks for successful DX (digital transformation), big data & analytics as well as Cybersecurity are the key.
• As part of analytics, it is important to draft a clear vision, identify applications, understand commercial levers and operating model.
• By adopting a proactive stance towards cyber resilience and implementing the seven steps outlined above, businesses can fortify themselves against the ever-evolving threat landscape and safeguard their digital assets from malicious actors.
• Just as in the perpetual battle of coding and decoding, the race for cybersecurity continues unabated, demanding vigilance, innovation, and unwavering commitment from all stakeholders.