{"id":59331,"date":"2021-07-13T09:32:11","date_gmt":"2021-07-13T16:32:11","guid":{"rendered":""},"modified":"2022-04-11T13:47:19","modified_gmt":"2022-04-11T20:47:19","slug":"just-get-started-accelerating-a-data-culture-in-your-organization","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/industry\/microsoft-in-business\/business-transformation\/2021\/07\/13\/just-get-started-accelerating-a-data-culture-in-your-organization\/","title":{"rendered":"Just get started: accelerating a data culture in your organization"},"content":{"rendered":"

\"womanI recently spent an hour with Mike Lampa at Great Data Minds, talking about enabling digital and data transformation<\/a> in the enterprise. The entire conversation is worth a listen. However, I wanted to highlight a few key enablers to accelerate a data culture.<\/p>\n

Mike mentioned that he still sees many enterprises struggling to make progress, even though data and analytics will be a significant component of digital strategy according to any research you look at from Gartner, McKinsey, and the like. I had a few thoughts about what\u2019s holding them back and how to speed up the transition.<\/p>\n

Change leadership is critical<\/h3>\n

It takes time to switch from making gut-based decisions to making decisions based on business data. This behavior change needs to be endorsed and supported from the top through executive sponsorship. If leaders are not driving this standardization, it will be difficult to compel adoption.<\/p>\n

When everyone is creating their own dashboards, spreadsheets, and reports, the enterprise isn\u2019t talking the same language on the same set of numbers. That creates confusion, inaccuracy, and, more importantly, ill-informed decisions.<\/p>\n

Instead, everyone needs to come together around one version of truth, looking at the same key business metrics. With a visible sponsor \u201cletting the data tell the story,\u201d everyone has the same baseline for what\u2019s happening, allowing everyone to focus on why it’s happening and, more importantly, what to do about it. This \u201clean-in effect\u201d reduces the resistance to change.<\/p>\n

Diversity is welcome<\/h3>\n

Cultivating a culture of learning is also strongly tied to diversity and inclusion in hiring processes. Differing points of view bring constructive tension to question decisions and look at things more holistically. A data culture means allowing more people with varying perspectives to have a voice.<\/p>\n

Skills enable a data culture<\/h3>\n

Emerging technologies require new skills. Right now, most universities have a data science program that will educate the next-generation workforce. They will be fundamentally grounded in Python, SQL, and other languages, and they will have business and statistics courses to get the domain context for meaningful business conversations.<\/p>\n

In the meantime, we need to reskill the current workforce. There will always be change agents who are pushing the curve and looking for new ways of solving problems, but we need to grow that cohort through training and create space for that training.<\/p>\n

One core focus for Microsoft that addresses the skills gap is engineering our products to use the skills of today backed by next-gen technologies and platforms. We build services on top of game-changing technology breakthroughs like machine-learning algorithms and platforms like Apache Spark to make them much easier to adopt.<\/p>\n

For example, Microsoft Azure Synapse Analytics<\/a> delivers big data analytics as a service in the cloud, and Microsoft Azure Data Factory<\/a> is a visual drag-and-drop approach for data transformation. The user doesn\u2019t need to know that there is sophisticated technology behind these tools; they just need them to be intuitive, reliable, and performant.<\/p>\n

This way, organizations can let Microsoft carry the weight of very low-level technologies so they\u2019re getting the greatest capabilities using a more familiar interface, such as Microsoft Power Query<\/a> or Azure Machine Learning<\/a>, to get that benefit.<\/p>\n

Encourage the adoption of data tools and processes<\/h3>\n

If your enterprise has deployed analytics or machine learning tools but they aren\u2019t being used, it\u2019s important to figure out how to integrate them into your business processes and workflows with as little disruption as possible. Applied AI is a key enabler of digital transformation.<\/p>\n

Consider that your frontline workers may have been in the organization for five, 10, or 20 years, and changing the way they perform day-to-day puts pressure on them. They\u2019re accountable to deliver on their jobs, helping customers or selling products. Changing a business process that\u2019s core to their job function has a significant impact on their morale and productivity and on the company\u2019s performance and reputation.<\/p>\n

There are a few ways to ease the friction of adoption. One is to bring in products and services like the ones they have been using for a long time, which lowers the technical bar. For example, this is a big reason why Microsoft Power BI and Microsoft Excel are so similar (and so well-adopted).<\/p>\n

Another way to ease friction is to make data insights seamless to the frontline experience. For example, present the predictive models within the ERP or CRM system they are already using, so they are not changing the way they do their job and they are using the same interface they have used for years. Change as little as possible so adoption increases. Increased adoption will provide better insight into what is a beneficial change or a counterproductive change.<\/p>\n

Establish a strong relationship with your business and technology partners<\/h3>\n

We\u2019ve all heard the nightmares of the \u201cif you build it, they will come\u201d approach. Modern data practices, machine learning, and architecture are important, but not at the expense of business goals and needs. Few organizations are in the business of data management, so make sure that your efforts are aligned with company priorities, initiatives, and strategic investment areas.<\/p>\n

This alignment will help you get stakeholder buy-in, budget, adoption, and prioritization. It may even help you win over a \u201cshadow group\u201d by leaning in together to accomplish a mutual goal. Remember to focus on high-impact, low-risk, low-effort use cases to establish a track record of success and show why this data culture is different than the \u201cold way of doing things.\u201d<\/p>\n

Get started with data transformation<\/h3>\n

Finally, the most important message is to just get started. You do not have to pick the perfect tool or know exactly how you are going to get the data. You can try something in the cloud, and in a day or two, you will know whether it is yielding fruit or not. If it is not, throw it away and move on to the next business-affecting idea. Agility is crucial to success. Analysis paralysis delays value.<\/p>\n

The key is to find out how you can get velocity and agility with your business processes and be able to rely on a cloud scaler like Microsoft to supply the platform you can build on.<\/p>\n

Do check out the full hour-long video on data culture<\/a>, where Mike and I cover topics from the impact of mergers and acquisitions on data to the effects of SaaS\/silo proliferation to capturing the voice of the customer.<\/p>\n

To learn more about what sets data-focused organizations apart and enables them to outperform on digital transformation, you can also read the report The Culture of Data Leaders<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

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