Editor’s Question: How does your company plan to improve its data analytics?

Editor’s Question: How does your company plan to improve its data analytics?

It would be at a company’s peril to ignore its data. Data provides valuable insight for businesses into what their customers want and how to streamline operations. In a recent article for Intelligent CXO, Barry O’Donnell, CTO, TSG, said:Utilising data-driven strategies is essential because it enables businesses to forecast future trends and make informed decisions, which is crucial for both emerging start-ups and established businesses.”

Data can make the difference between being a successful company or not. The most important part is to harness your data, whether you are a start-up or a large business. Things to consider are how do you store your data? How do you collect and analyse it? Companies can consider using an IT partner to make sure this is done correctly. O’Donnell said: “According to Gartner, businesses that harness their data correctly are five times more likely to succeed over competitors. Ensuring that you’re gaining the best perspective for decision-making by understanding your business metrics and market trends is a must for a strong business.”

Every business will be looking for different results with its data, but it can help make decisions, increase profits, improve processes and increase customer satisfaction.

Suki Dhuphar, Head of EMEA at Tamr, believes a data product manager is invaluable. He said: “Essentially, data products represent the best version of a company’s data. They are clean, curated and trustworthy datasets that make data tangible and accessible to everyone within the organisation, fostering more accurate insights for business decision-making. 

“We advise employing a data product manager to maximise the value derived from data products. Responsible for developing and delivering data products, a data product manager ensures data product implementation and, arguably even more crucially, that the data products provide tangible value.”

Data can be used to show stakeholders how the business is doing. It can also be used to explore emerging markets.

Data governance is also very important as it includes putting measures in to protect stored and transmitted data and creating strategic data access policies. These policies will help to avoid data breaches, which is all too common. Apricorn, a leading manufacturer of software-free, 256-bit AES XTS hardware-encrypted USB drives, recently announced findings from annual Freedom of Information (FoI) responses into data breaches and device loss within government departments in the UK. The results highlighted an alarming number of customers potentially affected by breaches declared to the Information Commissioner’s Office (ICO) by the HM Revenue and Customs (HMRC) during 2023.

HMRC noted that the number of customers potentially affected by the 18 breach reports on notifiable incidents disclosed to the ICO totalled 10,209. This is concerning given the sensitivity of the data that HMRC houses.

Three experts on the following pages discuss data analytics and how they can be improved.

Thierry Nicault, Area Vice President and General Manager, Salesforce Middle East:

Artificial Intelligence promises to transform every aspect of business operations, yet a lot of companies lack clarity on how to get from pilot to full production and value realisation. In today’s digital landscape they struggle with islands of data spread across various systems, leading many workers to not trust the data used to train AI systems and experience difficulty to get what they want out of them.

The future of enterprise AI isn’t about more data – it’s about the right data. When AI is grounded in a company’s own data, it delivers more useful results and ultimately drives greater trust and adoption.

Only by consolidating their data will companies be able to fully understand the complete customer journey. A trusted data foundation and integrating AI into workflows across the enterprise are key ingredients needed for AI success.

Deploying these together, companies can unlock enterprise deployments at scale and drive measurable outcomes from AI automation, personalisation and performance optimisation, including higher sales productivity, faster customer service resolutions and higher-conversion marketing campaigns.

Building a trusted data foundation

For AI to live up to the hype, Large Language Models (LLMs) must be grounded in trusted enterprise data. However, with data trapped in disconnected silos, wholesale Digital Transformation and value realisation remains elusive. Prospects are worse when the data being used to ground AI models is incomplete, incorrect or irrelevant — leading to inconsistent, incorrect results.

Unlocking the power of trapped data enables better analysis, decision-making and AI automation, grounding customer and business data and metadata – a common language that integrates all applications – in ways that deliver trusted, outcome-oriented results without expensive model training.

Take, for example, real-time data that a prospective customer has just visited a company’s website. Previously, sales reps would have had no way of knowing this without manually pulling data into a custom report. Real-time data brings actionable insights, allowing for immediate customer engagement, resulting in higher conversion rates, revenue growth and customer satisfaction.

Trust is a key component of successful enterprise AI deployments. By unifying and cleansing their data, companies can ensure that AI models operate on the most accurate information. 

Integrating AI into the flow of work

The need to deliver AI in the flow of where companies’ sales, service, marketing, commerce, developer and other employees work explains why they’re leaning into conversational assistants, for their employees to interact with any data or workflow across their enterprise.

With specific customer data, employees can generate useful responses which are automatically grounded in all of their organisation’s trusted data and metadata. From generating customer campaigns, to answering service questions, everything is personalised, based on consolidated data – all securely within the confines of their company’s data and business processes.

The powerful combination of data and CRM makes these personalised customer experiences possible. For today’s consumer, milliseconds matter. The cost of not keeping up with them could be lost sales opportunities, poor social media reviews, or a disconnect in healthcare delivery.

While Generative AI is still in its early stages for most companies, the potential for true enterprise transformation is immense. Those that can put in a foundation of data and trust and offer AI in the flow of where their employees work, will be able to shift from pilot to production and realise tremendous value, employee satisfaction, customer loyalty and business growth.

John Abel, SVP & Chief Information Officer, Extreme Networks:

The ever-growing volume of digital data creates a challenge: keeping track of it all. To make informed decisions and optimise outcomes, organisations need a holistic view into their various data sets. That’s why we’re laser-focused on centralising our data. Internally, we’ve combined datasets that are usually siloed – like sales, renewals data, usage data, customer success metrics and even call centre support data – to gain a full picture of how our customers are interacting with us. This complete picture empowers us to continuously deliver better experiences for customers and unlocks opportunities for things like contract renewals, which is a win-win for everyone.

Our data focus extends beyond individual products. We’re also integrating network performance data with other crucial aspects like facility footprint and security measures. Again, this more holistic view allows us to identify potential issues before they snowball into major problems. Predictive AI algorithms play a key role here, enabling us to anticipate and prevent disruptions.

What’s more, we’re all about making data more accessible. By centralising our data sources and using AI tools, we can get answers to important questions much faster. This empowers everyone – customers, partners and internal teams – to access the information they need quickly and easily. We’re currently implementing this approach with product data, HR and benefits data and sales performance data.

But it’s not just about gathering loads of data. While data volume is important, quality is key. Inaccurate data leads to flawed analysis. To ensure the validity of our insights, we meticulously collect and manage our data, employing rigorous processes and continuous refinement to maintain optimal data quality.

Our data analytics approach hinges on three core principles: integration, innovation and unwavering commitment to quality. By harnessing the power of data responsibly and prioritising data integrity, we’re confident in taking our analytics capabilities to the next level. This translates to a significant advantage for us, but more importantly, it empowers us to deliver exceptional value to our customers.

Phil Trickovic, SVP of Revenue, Tintri:

In today’s business landscape, the ability to leverage disaggregated data sets autonomously is not merely advantageous but essential for business survival. Vital components of autonomous platforms, intelligent data management and the significance of real-time analytics are essential in driving success in the digital era. As such, organisations need to observe the following:

1. Mitigating platform entropy: embracing automation and predictive analytics

In the contemporary AI-driven environment, optimising real-time workloads autonomously is paramount. Automation, combined with real-time predictive analytics tailored to workload profiles, empowers organisations to maintain a competitive edge. By capturing, analysing and adjusting working data sets, businesses can forecast trends, identify efficiencies and provision resources accurately and autonomously.

2. Addressing integration challenges

In today’s interconnected business ecosystem, siloed data and platforms hinder progress. Implementing ‘workload-aware’ platforms breaks down these barriers, providing a real-time holistic view of operations. Actionable workload management platforms unlock numerous time and cost-saving capabilities, facilitating seamless integration across the organisation.

3. Implementing intelligent data architecture and management platforms

As global processing capabilities expand, it’s imperative to control costs while maintaining quality. Autonomous tuning based on real-time workload profiles offers precisely this capability. Tintri’s commitment to providing unparalleled intelligence and autonomous scalability remains unwavering.

4. Ensuring data compliance (e.g., GDPR)

In an era marked by heightened data privacy concerns, compliance is not just a choice but a legal and ethical necessity. Regulations like GDPR establish rigorous standards for data protection and privacy, with non-compliance carrying significant consequences. Organisations must therefore ensure their data management systems are sufficient to ensure compliance – from data encryption, recovery, retention and auditing/reporting. By proactively adhering to these regulations and implementing robust compliance measures, organisations safeguard customer rights and uphold ethical data practices.

5. Prioritising data protection and recovery

The stakes for data protection and recovery have never been higher. Ability to recover operations is of the utmost criticality. Given the ever-evolving cyberthreats and potential catastrophic costs of data breaches, Tintri’s relentless commitment to identify and mitigate potential threats before they are of impact is in our DNA. 

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