{"id":51652,"date":"2026-10-26T14:33:28","date_gmt":"2026-10-26T13:33:28","guid":{"rendered":"https:\/\/implema.se\/news\/start-with-business-value-not-with-technology\/"},"modified":"2026-11-02T14:48:02","modified_gmt":"2026-11-02T13:48:02","slug":"start-with-business-value-not-with-technology","status":"publish","type":"post","link":"https:\/\/implema.se\/en\/news\/start-with-business-value-not-with-technology\/","title":{"rendered":"Start with business value \u2013 not with technology"},"content":{"rendered":"\n

The opportunities to collect, combine, and analyze data have never been greater. With modern data platforms and AI, companies can access information from a wide range of systems and create entirely new decision-making foundations. But more data sources, more advanced analytics, and new technical possibilities don’t automatically lead to better decisions or increased profitability. Susanne S\u00f6derholm at Implema believes many companies need to rethink where the work should begin. <\/p>\n\n

\u2013 We need to start by asking ourselves what we want to get out of the information. Which questions do we need to answer, and which decisions do we need to make? It’s not about collecting as much data as possible, but about identifying which information actually creates value. <\/p>\n\n

From technical possibilities to business needs<\/strong> <\/h2>\n\n

When companies want to develop their data analytics, it’s easy to start with the technology. What systems are involved? What information can we read in? What and how should this be visualized? And what opportunities does AI open up? <\/p>\n\n

These are relevant questions, but according to Susanne, they should come later in the process. <\/p>\n\n

First, the business needs to define what it actually wants to achieve. This could involve improving forecasts, reducing capital tied up in inventory, streamlining processes, or creating better foundations for strategic decisions. <\/p>\n\n

After that, you need to understand which relationships, business rules, and operational conditions affect the outcome. Only then is it possible to determine which data and technical solutions are needed. <\/p>\n\n

\u2013 There’s a tendency to want to pull in as much data as possible, simply because we can. But not all data adds value. If we don’t know what the information will be used for, we risk spending a lot of effort on something that doesn’t move the business forward, says Susanne.So it’s not about collecting as little data as possible, but about prioritizing the initiatives that bring the greatest benefit. <\/p>\n\n

<\/p>\n\n

From business value to data foundation<\/strong> <\/h2>\n\n

Susanne’s reasoning can be summarized in three steps: Business Value First, Context First, and Data Foundation. <\/p>\n\n

\"\"<\/figure>\n\n

The first step is about defining what business value you want to create. Next, you need to understand the business’s processes, relationships, and business rules to determine what information is required. Finally, it’s about ensuring the information has the right quality, structure, traceability, and security to be used reliably.<\/p>\n\n

This doesn’t mean companies should build their data foundation from scratch for every new initiative. On the contrary, it needs to be sustainable in the long term and able to support many different needs. But when a new initiative is being planned, business value should guide which efforts are prioritized.<\/p>\n\n

Business value sets the direction. Business context clarifies the needs. The data foundation makes it possible to put them into practice.<\/strong><\/p>\n\n

A stable data foundation remains essential<\/strong> <\/h2>\n\n

Starting with business value doesn’t mean data quality, structure, and governance become less important. On the contrary. <\/p>\n\n

Companies need sustainable, long-term data platforms where information can be quality-assured, managed, and reused for different purposes. But every analytics or AI initiative doesn’t need to start by refining all available datasets. <\/p>\n\n

One example of how Implema works is the so-called medallion architecture<\/a>, where data is managed across three layers. <\/p>\n\n

In the bronze layer<\/em>, information from various data sources is collected in as close to its original state as possible. In the silver layer<\/em>, the information is processed and quality-assured, while common definitions are established. In the gold layer<\/em>, it’s adapted to the business’s needs, for example through calculations, metrics, KPIs, as well as for visualization and prompting, so that everyone works from the same version of the truth. <\/p>\n\n

An important part of this is traceability. Through data lineage<\/a><\/em>, it’s possible to track the origin of information and how it has been processed. Access rights and security ensure that the right people have access to the right information. <\/p>\n\n

\u2013 When someone looks at a metric, it should be possible to understand where the number comes from and how it was calculated. That’s a prerequisite for trusting the information and using it as a basis for decisions, Susanne explains. <\/p>\n\n

The point is that the technical solution should be designed to meet real needs, while the data foundation is robust enough to support future use cases as well. <\/p>\n\n

From data to usable decision-making foundations<\/strong><\/h2>\n\n

At a customer in the e-commerce and retail sector, Implema is currently working to combine information from several data sources and make it available in Power BI. <\/p>\n\n

The work includes common definitions, traceability, and access control. This makes it possible to create a more cohesive basis for decision-making while ensuring that different users get access to relevant information \u2013 and only the information they actually need. <\/p>\n\n

The example illustrates an important principle: the value doesn’t lie in how much information can be gathered in one place, but in how well it can be used to support the business’s decisions. <\/p>\n\n

The same principle becomes increasingly important as companies start using AI. The technology opens up new ways to analyze information and automate tasks, but it still requires an understanding of which problems need to be solved and what conditions apply. <\/p>\n\n

<\/p>\n\n

Business value is the measure of success<\/strong> <\/h2>\n\n

As technology becomes increasingly accessible, it’s easy to measure progress by the number of integrations, data sources, reports, or AI applications. <\/p>\n\n

But these aren’t necessarily the metrics that show whether the business has actually improved. <\/p>\n\n

A successful initiative should instead be linked to concrete improvements: faster and more reliable decisions, more efficient processes, better resource utilization, or new business opportunities. <\/p>\n\n

For Susanne, it ultimately comes down to letting business needs guide the use of technology \u2013 not the other way around. <\/p>\n\n

\u2013 Tomorrow’s competitive advantage won’t be about who has the most data or the most advanced AI technology. It will be about who is best at using the right information in the right context to create real business value, concludes Susanne S\u00f6derholm. <\/p>\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

The opportunities to collect, combine, and analyze data have never been greater. With modern data platforms and AI, companies can access information from a wide range of systems and create entirely new decision-making foundations. But more data sources, more advanced analytics, and new technical possibilities don’t automatically lead to better decisions or increased profitability. Susanne […]<\/p>\n","protected":false},"author":22,"featured_media":48203,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[2721,2586,2720,2518],"tags":[2583,2816,2587,2596,2817,2818,2550],"class_list":["post-51652","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-expert-article","category-featured-news","category-insights","category-news","tag-ai","tag-ai-first","tag-analytics","tag-business-intelligence","tag-business-value-first","tag-data-first","tag-digitalization"],"acf":[],"_links":{"self":[{"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/posts\/51652","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/users\/22"}],"replies":[{"embeddable":true,"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/comments?post=51652"}],"version-history":[{"count":3,"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/posts\/51652\/revisions"}],"predecessor-version":[{"id":51655,"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/posts\/51652\/revisions\/51655"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/media\/48203"}],"wp:attachment":[{"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/media?parent=51652"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/categories?post=51652"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/tags?post=51652"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}