Hosts Chris Gregg, Avinash Walvekar, and Rashmi Diwadkar from UXLI delve into the very real data challenges companies face every single day.
In this episode, we tackle the critical question: Why do organisations, despite knowing data is vital, hesitate to adopt newer solutions, and how can they overcome these fears without burning through their budgets?
Many companies feel like they're drowning in data because it's often scattered, inconsistent, and sometimes unreliable across multiple systems. For example, manufacturing companies store production data in SQL, CRM data in Salesforce, ERP data in Dynamics 365, and customer feedback in Excel files – but these systems often do not talk to each other, preventing vital insights and reports. Similarly, a mid-sized bank faced manual, slow, and error-prone statutory reporting due to credit risk and customer onboarding data being in disparate systems.
The primary reasons for this fear and hesitation among leadership typically revolve around three key concerns:
• Cost: The assumption that fixing data problems means a multi-million-pound project and investment.
• Complexity: Fears of integrating too many technologies and IT projects dragging on for months or even years, based on "horror stories".
• Disruption: The concern about disturbing the "business as usual" (BAU) phase and losing business continuity when switching systems.
Beyond these financial and logistical concerns, deeper issues contribute to the dilemma:
• Data Illiteracy: A lack of understanding across the organisation about what to do with data, making new platforms intimidating rather than empowering.
• Lost in Communication: Decision-makers from IT and business often do not collaborate to define KPIs, desired systems, or improve data quality.
• Absence of a Data Product Owner: Unlike other products, data often lacks a singular person responsible for its quality, support, and ability to provide relevant KPIs for informed decisions. This human and cultural element is crucial, as data projects often fail due to human factors, not just tools.
But there's good news! Technology has evolved, and the barriers that once existed are diminishing. Companies can tackle these challenges without breaking the bank or causing chaos by:
• Starting Small: Instead of a risky "big bang" approach, focus on resolving one complex problem at a time and building solutions layer by layer. This approach led to success for a retail customer by integrating an ERP with a marketing campaign, building confidence and proving value in smaller chunks.
• Utilising Modern, Business-Friendly Tools: Solutions like Exelitics can connect to various data sources (ERP, CRM, databases, flat files) with drag-and-drop simplicity. This allows for faster insights at a fraction of the cost and can quickly integrate data for predictive analysis (e.g., predictive maintenance in manufacturing) without a huge upfront data science team.
• Empowering Business Users: Modern tools reduce dependence on IT teams, enabling business departments to do their own work, ensuring that insights are exactly what's needed without "lost in translation" issues.
• Adopting the Right Approach: Treat data as a product and nominate a dedicated data product manager who is responsible for its value and support. This "organised" approach can lead to inexpensive and fast results.
The real risk isn't trying something new; it's standing still while competitors move ahead. By taking smaller, safer steps that pay off faster, companies can evolve rather than revolve everything overnight.
If you're in a similar situation, pick one pain point, connect a couple of data sources, and prove the value – the rest will follow automatically.
Stay tuned for our next episode, where we'll be busting some data myths that refuse to die!
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