I believe that when you harness the streams of data being created, tie this to your existing data and apply AI to it, you enable better decision making and transformative processes. You will:

• Engage customers in new, meaningful ways​.
• Empower employees with the real-time insights they need to make the right decisions and be agile​.
• Optimise operations by better anticipating customer demands and supply chain disruptions​.
• Transform your products based on the signals from end users or the products themselves​.
• Meet and exceed sustainability commitments to have a better impact on the world around us.

You can see success in the way Marks and Spencer use their data. They own every part of their supply chain and collect data from across all touchpoints. They needed an agile solution that would scale, store and analyse their massive amounts of data. By replacing their on-premise data warehouse with Microsoft Azure-based data platform built around Azure Synapses Analytics, they gave teams access to valuable data they didn’t have before.

“By democratising access, we’re allowing more people to have ideas, and these ideas add incremental value to the business. Our retail support team is already reshaping how we report stock and stock loss to managers. Since they work closest to the retail side of the business, they know exactly how to consolidate and present information in ways that lead to real improvements, ” says Aaronpal Dhanda, Head of Data Technology.

The reality of data

As you can see, you can bring so much value to your organisation when you connect and manage your data properly. However, true modernisation comes by innovating the processes that run your business. This is where data realities begin to hit both IT and business leaders.

Most leaders agree that provisioning an end-to-end analytics platform is not a simple task. They need to make sure they can trust their data, that they can get deeper insights from it and it is bias-free. And, they need to ensure compliance along every step of the analytics journey.

All while trying to create agility and more rapid decision making by democratising data and tools to everyone and providing the digital upskilling they need to do this effectively.

The analytics paradox

More and more tools, systems and things become connected. As a result, companies must figure out how to manage and analyse new classes of data. Additionally, different lines of businesses see different values in data and analytics.

And this is what creates the paradox of analytics. Although analytics systems are intended to be centralised to provide “the single version of truth”, the more we apply new technology to integrate and analyse data in different ways, the more silos we can recreate.

Sometimes, through hard work to dissolve operational data silos we end up creating more. And not just data silos, but siloed teams and people. When we implement new technology for specific types of data, we are creating a more fractured approach to data integration. Then, this must be put back into the overall platform during the analytics lifestyle.

This approach is often sold as a centralised solution. But it is siloed architectures that creates siloed data teams. As a result, data governance becomes extremely difficult to accomplish. All counter to our objective to enable deeper analytics collaboration between teams.

Bring your vision to life with analytics

Every organisation has experts in both data and analytics technology. When they collaborate over data with the same efficiency they do for productivity applications, their expertise is utilised across team and organisational boundaries.​

Bristol City Council serves more than 400,000 people, with social care playing a huge role in their services. The children services team wanted to see a holistic view of the citizens they work with. This meant they needed to create a secure common data platform. By using Azure Data Lakes and Azure Data Factory, they unified that data to create a single view of each child across the disparate systems.

“The Microsoft Azure solution has been revolutionary for Bristol City Council,“ says Simon Oliver, Director of Digital Transformation. “We are now able to see the outcomes of the decisions we give. And by being able to do that at scale we’re able to make decisions based on what will happen.”

By connecting, managing and governing your data assets in a cohesive fashion, you have the foundation for successful digital modernisation. A simplified framework allows all of your data and all types of data to exist on the same platform and be governed in the same way.​

Azure Synapse and Azure Purview provide a single cloud service with a single interface for development, management, monitoring, security and governance of your data. You can link to your Dataverse to run advanced analytics on data from Dynamics 365 and Power Platform simply. This allows you to:​

• Be agile to the needs of the business or react to unpredictable ​external changes.
• Enable quicker insights for decision making​.
• Impact business models and overall value chains​.
• Get more granular, deeper insights.

And, most importantly, it is done in a secure, compliant and governed way.

Drive analytics value

Free your people to focus on real business value by managing the complexity of architecture on a single unified platform. This gives every person and every team in your organisation powerful analytics and insight. So, you can go from trying to manage the analytics paradox to driving real change and value to your organisation.

Find out more

Building a data-driven organisation 

About the author

As an advocate of data-driven decisions, Robin has spent over two decades at Microsoft ensuring organisations have the tools to leverage the zettabytes of data available today to achieve their digital transformation vision.

Microsoft has been on its own digital transformation journey for several years and data has been a central part of that journey. Robin focuses on creating a data-driven culture across the business at Microsoft. This includes ensuring that we are considering data across our internal processes, as well as how we are helping our customers and partners succeed with data.

Robin is passionate about learning and collaborating with our customers and partners about how to truly leverage data and AI to create new solutions.

Prior to working at Microsoft, she served in the US Military. She strives to bring her best in all aspects of work and personal life. From obtaining two law degrees and multiple professional certifications – all while working full time, parenting her daughters and balancing personal commitments (including training for an IronMan), she believes anything is possible.

The post Empower employees by unifying your analytics and data architecture appeared first on Microsoft Industry Blogs - United Kingdom.

To build and maintain trust with citizens we – as a data community – have an obligation to address these ethical issues. Previously, I’ve talked about how to build and effective data strategy and culture. A critical aspect of both strategy and culture is to ensure the ethical and responsible use of AI and data. We need to empower organisations to use data with a sense of responsibility. The EU recently released their Artificial Intelligence Act, the first legal framework for AI. In it, they take a risk-based approach to protect EU citizen’s rights while ensuring they can still foster innovation. As we saw with GDPR, the AI Act includes fines for infringements of up to four percent of global annual turnover (or €20M, if greater). Therefore, it is more important than ever to focus on the responsible use of data and AI.

Build your responsible AI strategy with the right question

Are you using AI technology to do the right things? Is it answering the right problems in the right way? AI shouldn’t be implemented because it’s a shiny new piece of technology. It should be used to help solve a problem. And to work properly, it needs to reflect the community you serve. To do this you need to build your data and AI solutions on ethical principles that put people first.

At Microsoft, one of my focusses as Chief Data Officer (CDO) is to ensure our use of data and AI remains ethical and responsible. What I have found is this is just as much a culture shift as much as a technological process. In a recent webinar, when I spoke with other data leaders across the industry, they also agreed.

What was clear across the board is that organisations need to take a very practical approach to responsible data and AI principles. Below are six principles that organisations can use to build their own responsible AI governance.

1.      Fairness

Although our society is diverse, it is unfortunately unfair and bias. It is our role to ensure that the systems we develop and deploy reduce this unfairness. However, fairness doesn’t just relate to the technical components of the system. It also about the societal context in which it is used.

“Ensuring the biases are taken care of is important. We think about how data is being increasingly used across platforms and avoiding any disproportional impact as a result,” says Sudip Trivedi, Head of Data and Analytics at London Borough of Camden.

How can leaders ensure fairness? We need diverse teams that question the data and models we are using at every step along the journey. We need to think critically about the implications and unintended consequences more broadly. Having checklists to continually monitor data and AI processes is a great way to ensure we stay fair. Leverage tools and learnings to validate fairness regularly.

Fairness tools:

AI fairness checklist

Datasheet fairness checklist

Fairlearn open-source toolkit

2.      Inclusiveness

Our aim at Microsoft is to empower everyone to achieve more. We are intentionally inclusive and intentionally diverse in the paths we take. AI needs to be built with everyone in mind. Because when you design solutions that everyone can access, the data you collect will be fairer.

This is where your diverse organisation becomes a huge benefit to you. By ensuring that your data and AI teams are diverse you will be building for everyone. And don’t forget to include a diverse audience for your testing to ensure that your systems remain accessible for all.

“It takes having that diversity within your organisation or stakeholder group to spot issues,” says Nina Monckton, Head of Data Strategy, Advancing Analytics & Data Science at AXA Health.

Inclusive tools:

Inclusive design guidelines

Design with accessibility in mind

3.      Reliable and safe

Our data and AI processes need to be consistent with our values and principles. As owners of these models, we need to continuously check that they’re not causing harm to society. And if they are, we need to have processes to fix them. We’re also transparent with our users on these issues.

Building reliable and safe AI isn’t limited to just physical systems that affect human life. For example, self-driving cars or AI in healthcare. It’s also about ensuring that every model you create stays reliable and safe no matter how big it gets or how many people work on it.

Reliable and safe tools:

Accelerate the pace of machine learning while meeting governance and control objectives with MLOps

Preserve privacy with Project Laplace

4.      Transparency

Transparency can help us reduce unfairness in AI systems; it can help developers debug systems, and it helps us build trust with our customers.

Those who are creating the AI systems should be transparent about how and why they’re using AI. They should be open about the limitations of their systems. People should also be able to understand the behaviour of AI systems.

“Being transparent is critical to doing good data work. If you don’t have the transparency, it’s very difficult to know if it’s doing its job well,” says Daniel Gilbert, Director of Data at News UK.

To truly understand AI, we need to democratise through digital skilling. This is not just within your organisation, but within society too. We need to work together to help encourage skills growth across our communities with digital skilling programmes. This will help further increase diversity in our organisations as we introduce people to the opportunities of technology careers.

“A lot of the data we are collecting and using are from people who are digital literate. There’s a real hard question: Is the data we’re collecting really representative of the people we’re trying to provide services for?” says Nina.

Transparency tools:

Microsoft Learn

Improve digital skills

Bridging the digital divide

5.      Privacy and security

Privacy is a fundamental right, and it must be built in to all our systems and products. With AI, machine learning and the reliance on data, we add new complexities to those systems. This adds new requirements to keep systems secure and to ensure data is governed and protected.

You must think about where and how the data is coming from. Is it coming from a user or a public source? How can your organisation prevent corruption and keep the data secure?

Privacy and security tools:

Learn about confidential computing 

6.      Accountability

As leaders, we are accountable for how our systems impact the world. Let’s look at facial recognition. There’s a lot of good uses for it, but only if we stick to principles that guide on how we develop, sell, and advocate for regulation on facial recognition.

Accountability includes internal and external factors. We need to keep key stakeholders informed across the whole cycle of AI systems. And we need to ensure we stay accountable to society.

Mahesh Bharadhwaj, Head of Europe Analytics at Funding Circle talks about asking the right questions at the right time: “Are we using the AI to do the right things? Do we check the models are being built correctly? Are we making sure the model is being deployed on the context it is built?”

Accountability tools:

Explore interaction guidelines 

Responsible AI builds trust

To build trust, a balance between culture and data capabilities is key. We need to make sure we are encouraging people to leverage data in ethical and responsible ways. These six principles should help you build AI-systems while building a diverse and inclusive culture. By doing this, we will ensure we’re serving our community in the best way possible.

Find out more

Discover our approach to responsible and ethical AI

Build a modern data strategy

Resources to empower your development team

Register for Microsoft Build on 25-27 May 

About the author

As an advocate of data-driven decisions, Robin has spent over two decades at Microsoft ensuring organisations have the tools to leverage the zettabytes of data available today to achieve their digital transformation vision.

Microsoft has been on its own digital transformation journey for several years and data has been a central part of that journey. Robin focuses on creating a data-driven culture across the business at Microsoft. This includes ensuring that we are considering data across our internal processes, as well as how we are helping our customers and partners succeed with data.

Robin is passionate about learning and collaborating with our customers and partners about how to truly leverage data and AI to create new solutions.

Prior to working at Microsoft, she served in the US Military. She strives to bring her best in all aspects of work and personal life. From obtaining two law degrees and multiple professional certifications – all while working full time, parenting her daughters and balancing personal commitments (including training for an IronMan), she believes anything is possible.

The post 6 ways leaders can build responsible AI and data systems and the tools that can help appeared first on Microsoft Industry Blogs - United Kingdom.

Start your data strategy with a goal

To build an effective data strategy, the first thing to do is think about what you want to achieve. Analyse business functions or goals where data could provide a tangible result. I often suggest to customers to start small, with one project. Use that to learn new skills and explore your data. Use the learning from these projects to iterate and build towards a successful integrated data strategy. This way you will gain first-hand knowledge and skills, while dealing with the practical and operational tasks necessary for success.

Do the groundwork for your data strategy

By 2025, IDC research expects data to reach 175 zettabytes. You may not have this much, but you’ll find you have collected a lot of data. Once you have your business goal, take some time to look at your data. Here’s three data realities you’ll need to be aware of when building your strategy:

Research from Harvard Business Review and Microsoft found that 52 percent of respondents say inaccurate and insufficient data is a key business challenge. To ensure you get the best insights out of your data you need to take stock of what you have and what you want to do with it. In fact, data preparation is about 80 percent of the work in data analysis.

Build innovation excellence with data

This same Microsoft research found that 55 percent of respondents believe data silos and managing data from multiple new systems are an organisation’s biggest challenge. But we know that bringing together these datasets are what can help organisations innovate and build competitive advantage. That being said, we have to ensure we build a data strategy using a best-in-class approach.

A best-in-class approach to data strategy:

Data modernisation is key to addressing and fixing data silos. Connect your data together across the business with tools such as Dynamics 365. This will help not only reduce silos but build new insights from the collective data that you wouldn’t get if they were segregated.

Cloud-native apps support high performance at any scale. Take advantage of the power of the cloud capabilities to truly use your data and reduce those tedious tasks for employees – helping them spend more time on value-add work.

Analytics are needed to truly understand customer behaviours, operational processes, and to generate insights. When you have the right data as your base, analytics can help inform decision-making in a powerful way.

Data science then takes those insights and applies machine learning and AI to power experiences. But it isn’t just for the data scientists anymore. Thanks to low/no code solutions like Power Platform, employees without large coding experience can build workflows and solutions using data to improve their day-to-day operations. For example, a customer service team can build a Virtual Agent to answer frequently asked customer questions, so they can spend more time on complicated queries.

Data governance underpins any data strategy. You need to ensure you’re adequately protecting valuable business and customer data. This includes ensuring you have the proper regulatory compliance in place. A cloud platform like Azure has multi-layered, built-in security controls and unique threat intelligence to help you identify and protect against rapidly evolving threats.

It’s also important to think about how you use that data. The responsible and ethical use of data and AI is important for your reputation. It can also reduce bias and risk in your data, ensuring you’re delivering exactly what you need to better serve the community.

Build a data-driven culture

Fundamentally, a strategy won’t work unless you have the right culture. For a journey to be successful, everyone needs to take part in it.

Engage with your stakeholders and the senior leadership team from the start. Of course, this doesn’t mean you have full company meetings about your first project. Look at what problem you’re trying to solve and the teams you will engage along the way. Bring and inform the key stakeholders from those teams early on.

Another way of building culture is encouraging ‘champions’ to share their love and knowledge with their peers. This can be done by giving them the time and ability to set up workshops, employee groups, and even ‘hackathons’ to build a more grassroots approach to culture change. At Microsoft our yearly hackathons have bought product improvements, apps that improve accessibility like Seeing AI, and innovations to help sustainability, like machine learning models to assist in ocean clean ups.

It’s important to ensure every employee has the power to make impact-orientated decisions. After all, they’re the ones who know the business the best. By democratising access to data in a responsible way, you will empower employees to be more innovative, make better decisions, and ultimately, serve exceptional customer experiences.

Finally, build all employees skills to ensure they get the best out of data. According to our recent digital skills report, 63 percent of UK employees don’t think they have the appropriate digital skills. Building these skills is key to ensure they can confidently use data to make decisions, build innovation, and create new solutions. We have shared our learning paths, workshops, and on-demand training to help organisations re- and up-skill their employees on Microsoft technologies.

A people-centric approach to data strategy

Once you build your data foundation and connect it across your organisation, you will have a 360-degree view of your data. Take this further and join up external data to create an expanded view not restricted by your own organisational boundaries. And with a data-driven culture you can empower your organisation to leverage this to gain even more opportunities and insights, creating competitive growth.

One thing I’ve learnt from our journey at Microsoft is the technology is the easy bit. Most data strategy projects are about the people – the cultural change. Ensure this by being transparent along the journey, engaging others, and building their digital skills. Remember a data strategy is not a one-and-done approach. It’s a continuous learning cycle, where everyone can take part.

Find out more

Visit the landing page: Reimagine data and analytics

Download the eBook: Build a data-driven organisation

Download the report: Discover a new model of competitiveness

About the author

As an advocate of data-driven decisions, Robin has spent over two decades at Microsoft ensuring organisations have the tools to leverage the zettabytes of data available today to achieve their digital transformation vision.

Microsoft has been on its own digital transformation journey for several years and data has been a central part of that journey. Robin focuses on creating a data-driven culture across the business at Microsoft. This includes ensuring that we are considering data across our internal processes, as well as how we are helping our customers and partners succeed with data.

Robin is passionate about learning and collaborating with our customers and partners about how to truly leverage data and AI to create new solutions.

Prior to working at Microsoft, she served in the US Military. She strives to bring her best in all aspects of work and personal life. From obtaining two law degrees and multiple professional certifications – all while working full time, parenting her daughters and balancing personal commitments (including training for an IronMan), she believes anything is possible.

The post How to power up your data strategy to build innovation appeared first on Microsoft Industry Blogs - United Kingdom.