To Add Value To Your Business, Don’t Treat Data And AI As Islands
By now, business leaders should know that data is the most valuable asset for them, and they should understand the importance of using artificial intelligence (AI) technology to transform the way their enterprises do business.
AI goes by many different names, such as predictive analytics, machine learning (ML), intelligent virtual agents, data mining, deep learning and neural networks. However, people tend to forget that what differentiates AI from other software is its ability to learn from data and experiences instead of being explicitly programmed. Therefore, AI is the capability of a machine to perceive, understand and reason to extend people and organizations’ abilities. It’s also a set of tools and approaches designed for people to augment our capabilities. AI is a fantastic technology to help us make predictions, optimize outcomes or adapt to external changes.
From my perspective, AI amplifies our ability to make judgments and improve efficiencies to let us focus on activities that only humans can do.
Getting Off The Island
Given the speed of the technology’s evolution, enterprises typically need guidance and assistance to continually evolve their products and services in a way they can address, transform and disrupt their specific industry challenges or gain market share in their ecosystems.
Unfortunately, my team frequently reports that enterprises often don’t know where the data is or deal with their data spread across big data lakes, data pipelines and data warehousing. Similarly, business units and teams in the same enterprise work in silos. They have difficulties discovering data and face several challenges in integrating their data with existing IT architecture and systems. In other words, they are living on an island, and they can’t fully translate the benefits of data into actionable analytics.
For those like me that need data points, according to Deloitte, 63% of executives working at large companies (500-plus employees) don’t believe their enterprises are insight-driven because of a lack of infrastructure and teams working in silos.
My advice to technology executives such as CIOs, CDOs, and CTOs is to work harder to bring together data integration, enterprise data warehousing, data lakes, ML and big data analytics. IT systems (e.g., ERP/CRM solutions such as SAP or Microsoft Dynamics 365) or business-reports/dashboards (e.g., Power Bi) should have the ability to query data on their terms, using serverless or dedicated resources. Also, enterprises should invest in integrating technologies with a unified approach that gives users control over data warehouses/data lakes and helps them accommodate data preparation management.
Based on my experience, AI only adds value to businesses when they have an end-to-end analytics strategy supported by a cloud platform. The platform should also be ready to help them perform as needed and on a large scale. In my role as CTO at Microsoft, together with my team, we are investing time in assisting enterprises to bring these worlds together. Ultimately this leads to enhanced competitive advantage and drives transformation.
Six Best Practices To Build Trust In AI-Based Systems
As I used to say to my customers, there is no digital innovation without a cultural transformation. Enterprises also need to promote a data-driven culture to make decisions at all levels within the organization, and trust is a crucial factor. To ensure people can trust in AI-based systems, as a technology executive, I been promoting the need to invest in the following six best practices:
1. Focus on the quality of data, rather than just capturing data, to ensure that we can trust it and avoid ending creating broken data products and experiences. In AI, the data quality is critical because if we use insufficient or inaccurate data to train ML models, we will end up with flawed AI models that struggle to learn.
2. Provide clear guidelines to annotate and label the data. When faced with unlabeled data or unreliable data labels, the model’s performance could be negatively impacted, and accuracy could be compromised. Unfortunately, from my experience, we know that enterprises tag less than 3% of their data, so there is a lot of room for improvement.
3. Put in place a data governance framework to facilitate the decision-making and authority for data-related matters. Usually, you should start by establishing roles and responsibilities to increase collaboration between business and IT. The framework should also include feedback loops and change control processes for governing data.
4. Protect your data with the most advanced security and privacy features on the market to keep it secure and diagnose potential security concerns as they happen.
5. Create an end-to-end AI-related solution and iterate on better accuracy to empower users to create real business impact.
6. Implement a robust responsible AI governance model, putting the people first when building AI systems.
I want to invite enterprises to get off the island by building the required bridges and distribution channels to allow end users and their company ecosystem partners to get the full value of AI.
The best strategy is one that can connect enterprise data and AI-related efforts to everything we care about and turn data into intelligent experiences.
To close, I want to leave you with the perspective of Satya Nadella, CEO of Microsoft, when asked about the role of AI in the recovery journey in the post-Covid-19 world: “AI and other digital technologies have ensured productivity and mission-critical activities during the pandemic. Digital transformation will no longer be an optional topic in board rooms. That is the kind of structural change we are seeing.”
This article originally appeared on forbes.com To read the full article and see the images, click here.
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