Mikon reaktio oli erityisen kiinnostava – hänelle nämä ominaisuudet
tuovat mieleen jotain hyvin tuttua.

“Tämähän on kuin 2018 – mutta parempi”

– Mikon tarina Podium Datasta. Mikko on pitkän linjan #Qlik-veteraani, ja kun hän ensimmäistä kertaa näki
uudet Data Products -ominaisuudet, hän hymyili tunnistavan hymyn.

Vuonna 2018 Qlik hankki yrityksen nimeltä Podium Data, ja Mikolla on siitä
omakohtainen kokemus – hän ehti työskennellä kyseisen tuotteen kanssa
ennen kuin se integroitiin Qlikin tuoteperheeseen nimellä Qlik Catalog.

Qlik Catalogin ydinajatus oli ratkaista se ikuinen ongelma: tekniset ihmiset ja
liiketoiminta ihmiset puhuvat samoista asioista eri kielillä. Mitä tarkoittaa
“asiakas”? Entä “tuote”? Catalogin avulla sekä data-ammattilaiset että
liiketoiminta ihmiset saattoivat määritellä ja arvioida käsitteitä samassa
paikassa – yhdessä. Mikon mukaan ajatus oli oikea ja tuote toimi, mutta oli
käyttäjän näkökulmasta kenties hieman tekninen. Visio oli aikaansa edellä.

Nyt visio alkaa näyttää todelta

Kun kävimme Mikon kanssa läpi Qlikin uusimpia julkaisuja, tunnelma oli selvä:
tämä on sitä, mitä 2018 tavoiteltiin – vihdoin kypsänä muotona.

Kolme asiaa erottuu erityisesti:

• Data saadaan kaikkien näkyville – Data Marketplace toimii yhtenä
ikkunana organisaation datoihin.
• Datan laatu selviää – Trust Score kertoo selkeästi, kuinka luotettavaa
data on. Ei enää arvailua.
• Dataa voidaan helposti jakaa eteenpäin – REST- tai OData-
connectorin kautta data liikkuu sujuvasti jatkokäyttöön.

Ja parasta kaikessa? Kaikki tämä löytyy yhdestä käyttöliittymästä: olennaiset
datatuotteet, niiden laatu, missä niitä käytetään, mistä data on peräisin ja
minne se kulkee – Data Lineage kertoo koko tarinan.

Miksi tämä on tärkeää juuri nyt?

Data ei ole enää vain raportoinnin raaka-aine. Kun tekoäly ja AI-agentit
hyödyntävät dataa yhä enemmän, datan laatu, löydettävyys ja luotettavuus
nousevat aivan uuteen arvoon. Jos AI saa syötteekseen huonoa tai
väärinymmärrettyä dataa, seuraukset voivat olla vakavia.

Qlik Cloudin uudet ominaisuudet vastaavat juuri tähän tarpeeseen. Kun
datatuotteet on määritelty selkeästi, niiden laatu on arvioitu ja ne ovat helposti
saatavilla – niin ihmisille kuin koneille – ollaan oikeasti valmiita tekoälyaikaan.

Yhteenvetona voinee todeta: hyvät ideat löytävät aikansa!

Podium Datan visio oli oikeassa. Se syntyi vain hieman ennen aikojaan. Nyt
teknologia, markkinat ja tarve ovat kohdanneet – ja Qlik Cloud vie tätä
kehitystä eteenpäin tavalla, joka saa kokeneenkin Qlik-ammattilaisen
hymyilemään. Mikolle se on selvästi palkitseva hetki.
Seuraamme kehitystä innolla Mikon kanssa ja autamme asiakkaitamme
Goodinilla ottamaan nämä mahdollisuudet käyttöön.

Terkuin, Mats (von Hertzen), GOODINilta

Kiinnostuitko? Laita meille viestiä!

Generative AI has emerged as a transformative force, impacting all facets of an organization and influencing existing tools and solutions. While it enhances efficiency, Generative AI introduces new requirements for data structures, security protocols, and corporate governance within organizations. Despite its capabilities, it’s crucial to note that Generative AI doesn’t assume responsibility for decisions and actions; that remains the responsibility of human operators.

To realize the full Return on Existing Investments (ROIe), organizations must ensure that users maximize the utilization of these tools and solutions. While significant investments are made in technology, equal attention should be given to nurturing the skills of the teams, the potential generators of profit. It is imperative to monitor how users leverage these investments actively. The real value lies not just in the technology itself but in how effectively it is utilized by individuals within the organization.

Data Empathy – a Holistic Approach

When investing in new technology, organizations must concentrate on three primary areas. The principal impetus for any investment typically originates from business needs and potential benefits. The technology team plays a pivotal role in narrowing down and selecting appropriate technologies for the identified requirements. In theory, collaboration between business and technology teams can yield flawless reporting systems and dashboards. However, the recurring challenge lies in the third crucial area – the organization.

The diagram above illustrates the common areas for any organization and their interdependencies. In Data & BI projects, collaboration between Business and Data & Tech teams is typical. However, the ultimate success is contingent upon whether people use the solutions, find them easily applicable in their roles, and whether the business demands their usage, all of which require continuous monitoring.

To achieve Return on Existing Investments (ROIe), it is crucial to identify user needs through Work Design. Understanding the information required for everyday tasks, assessing data skills and literacy, and providing targeted training and coaching are vital. Leadership, management systems, and organizational culture play pivotal roles in achieving ROIe, impacting the human element significantly. Without an organizational emphasis on data use, investments may not yield expected results.

The path to realizing the full return on existing investments involves focusing on the organization and its people, embracing Data Empathy.

Decision Dimensions – Aligning People, Processes and Data

Management Information Systems are 90% people said my professor in 90’s. Today we have so much new and existing tech that we might not remember this truth. We are blinded by expanding number of technologies and solutions and often users are not able to keep up with the pace of development. Introducing new systems alone is insufficient; attention must be directed towards role design. This entails understanding how work aligns with the new system, necessitating changes to fully realize the benefits of the investment.

Aligning People, Processes and Data. People within an organization occupy specific roles, belong to teams, and engage in processes where decisions take place. To enhance data utilization and identify gaps in both people’s capabilities and data availability, an understanding of work design is essential. Mapping out a user’s typical day, identifying decisions tied to processes, and determining data requirements for those decisions are critical. Data presentation should align with actionable intelligence requirements, meeting the demands of users and fostering motivation for increased data utilization. Importantly, it’s essential to identify the role of GenAI/LLM solutions in the user’s daily workflow, not just focusing on technical solutions.

In conversations with numerous leaders and data & BI professionals, it’s surprising how little attention organizations have given to their people. While many have heavily invested in Cloud Data Platforms, AI solutions, and BI fronts, acknowledging that people are the biggest challenge in reaping the full benefits of these investments, very few have taken tangible steps to bridge the gap between people and data.

Now it is time to do just that.

Business Intelligence (BI) has emerged as a cornerstone for organisations, providing tools and techniques to turn raw data into actionable insights. Something GOODIN’s Jarmo Rajala has been consulting on for two decades.

But what is the real-life impact of data and BI on decision-making, and how can AI enhance this process? This is the high-level topic of The AI MIXER GOODIN is hosting together with Nieve and Fusion Ecosystem this week.

GOODIN’s very own Atte Ailio has been on the implementation side leading processes of tech and BI investments for 20 years and has a clear idea of the issues related to actually getting people to get involved.

From a company perspective here are probably the corner stones of why leveraging BI and AI is important in the future:

1. Informed Decision-Making: The impact of data and BI on decision-making is profound. By analysing historical data and current trends, organisations can make informed choices about strategy, resource allocation, and operations at large and in detail. Real-life benefits include cost savings, improved efficiency, and better customer as well as staff experiences.
2. Predictive Analytics: AI plays a pivotal role in BI by enabling predictive analytics. Machine learning algorithms can forecast future trends and outcomes based on historical data. This empowers businesses to proactively address challenges and seize opportunities, leading to more confident decision-making. And as change is the only certain constant, having some predictive analytics to help you with your decisions is pretty invaluable.
3. Personalised Experiences: Data and BI are instrumental in understanding customer behaviour. AI-driven personalisation takes this a step further by tailoring experiences, products, and services to individual preferences. The impact is seen in increased customer satisfaction, loyalty, and revenue. And this does not always have to mean massive investments as we learned at the Google Cloud Summit.
4. Risk Management: Businesses can use data and BI to identify potential risks and vulnerabilities. AI algorithms can continuously monitor data streams and alert decision-makers to emerging threats. This proactive approach can mitigate financial losses and protect a company’s reputation.
5. Resource Optimisation: AI, when integrated with BI, can optimise resource allocation. Whether it’s managing inventory, workforce scheduling, or marketing budgets, AI-driven recommendations help organisations allocate resources efficiently and cost-effectively.
6. Real-time Insights: In a fast-paced world, real-time insights are invaluable. AI-driven BI systems can process data streams in real time, allowing businesses to respond to changing conditions instantly. This agility is vital in industries such as finance, e-commerce, and healthcare to name but a few.

And last but most definitely not the least:

Improved Quality of Life: The impact of data, BI, and AI is not limited to business alone. In healthcare, for example, these technologies facilitate early disease detection, leading to better patient outcomes. In sustainability questions data is vital.

As a concrete example of this is for instance the enhancing the quality of life on a personal level when thinking of the commute to home or work when Data and AI is utilised in urban planning. With the help of them it is possible to effectively optimise traffic flows hence reducing for instance commute times.

The available technologies empower organisations to make informed choices, optimise resources, and deliver personalised experiences. When combined with AI, the benefits are multiplied, enabling predictive analytics, real-time insights, and risk management.

The Learnings?

The synergy of data, BI, and AI is transforming industries. To remain competitive businesses must embrace this data & AI revolution BUT NEVER forget about the human in the middle.

Want to talk with us some more?