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Every year we like to share with you what we see as the most relevant developments in the field of data & analytics. No futuristic trends far removed from our bed, but things that are directly applicable in your daily practice. So that you can start working with this tomorrow if you so desire. That is what we find important at Cmotions, translating all the beautiful applications of data and AI into your daily reality, without embellishments and hypes, but with a focus on usability and adding value.<\/span>\u00a0<\/span><\/p>\n At Cmotions, we pay attention to the entire data value chain. This chain runs from \"what do we want and why\" (the strategy) to the retrieval and structuring of data (engineering), to the smart use of data to arrive at practically applicable insights process improvements. Therefore, the trends we discuss will also come from all these elements of the value chain. Focus in breadth is how we like to call it.<\/span>\u00a0<\/span><\/p>\n We see two main trends for 2024: 1) Generative AI and 2) Selfservice & Augmented BI. As you have come to expect from us, we tell you in clear language what this means for you, but no less important, what it takes to successfully deploy these two trends.\u00a0<\/span>\u00a0<\/span><\/p>\n To start right off with the elephant in the room, no trend list at the moment can obviously include Generative AI. 2023 was the year when Generative AI made a name and fame with a big bang to the general public. Even if you have nothing (to do) with data, you probably know what ChatGPT is, to name just one example of Generative AI. 2024 will be the year for many companies to get serious about it themselves, looking for valuable applications. Here, as far as we are concerned, controlled use is key. We see a lot of potential value, but also recognize the concerns that are expressed, such as in the <\/span>chamber letter<\/span><\/a> and the <\/span>State Vision AI<\/span><\/a> which were shared with the House of Representatives in late 2023 and early 2024.<\/span>\u00a0<\/span><\/p>\n Before we talk about Generative AI in more detail, let's explain what we mean by \"generative\" artificial intelligence (AI) and how it differs from \"normal\" AI. The AI that we have been using for many years is like a very smart colleague who is good at a specific task. For example, think of predicting when a part of a machine needs maintenance, what is the best price for a product today based on supply and demand, or which part of the customer base might want to cancel its contract. Generative AI is also a very smart colleague, but you can use this colleague on many more different tasks, not only because this colleague has much more knowledge and \"experience,\" but also because it can think of new things. And that new, smart colleague also speaks your language - business language instead of data science jargon - and understands at lightning speed what you mean. Some obvious examples of the use of Generative AI:<\/span>\u00a0<\/span><\/p>\n Generative AI has the potential to change the work of many forever. Explicitly not just the work of data scientists and analysts, who until now sat at the AI buttons within the organization. But rather the entire organization can benefit. What is important here is to take the organization well into the opportunities it offers and discover them together, so that Generative AI is not perceived as threatening. By using tools such as <\/span>Copilot<\/span><\/a> manual tasks can suddenly be automated and creative tasks can be supported so that employees come up with better creations faster. And the diligent programming work can suddenly be done much more effectively and efficiently, allowing for much faster initial prototyping of smart applications with data and AI. By 2024, this will be experimented with and accelerated implementation at many organizations. Also because widely used tools such as Microsoft Outlook, Teams, Powerpoint, Word and Excel will start offering these capabilities by default.\u00a0<\/span>\u00a0<\/span><\/p>\n Generative AI also enables organizations to finally extract value from <\/span>all <\/span><\/i>available data. There is still a lot not being done with information hidden in unstructured data. This includes information in files, reports, legal texts, logs but also customer conversations, audio recordings, video recordings, scans, photos... The AI models that became available in 2023 already offer great opportunities to extract value from these sources. And the rapidly succeeding developments in Multimodal Models (models that combine text, images, sound) and Mixture of Experts models (specialized models, cleverly combined) will open even more doors in 2024.\u00a0<\/span>\u00a0<\/span><\/p>\n By the way, in 2024 we see Generative AI primarily as an addition, not a replacement for more traditional application of data science and AI. The power of these methods also creates challenges of verification, validation and interpretation that deserve adequate attention. Forerunners in the market and organizations under special scrutiny (think government organizations) will need to engage in controlled use of Generative AI techniques from the start. How do we ensure that we deploy these techniques in such a way that we are in control of what comes out, how it impacts employees, customers and society? That we don't break copyright, we don't unintentionally discriminate, we continue to respect privacy and legislation in the data we use, and what we create and do with Generative AI continues to align with the organizational goals we have? After the hype, the stage is now set for many to responsibly generate value with AI.<\/span>\u00a0<\/span><\/p>\n <\/p>\n Self-service analytics is not exactly a new trend, but still one worth mentioning (again). After an earlier big hype that did little to live up to itself, Augmented analytics in particular is nevertheless bringing Self-service analytics closer and thus under the spotlight once again.\u00a0<\/span>\u00a0<\/span><\/p>\n Augmented analytics refers to the use of AI and machine learning to improve and automate the analytical process. The goal is to make analytics more accessible to people with different levels of expertise and to enable faster and more informed decisions. Augmented analytics integrates advanced analytics, predictive modeling and natural language processing to generate insights, make recommendations and identify patterns without requiring users to have in-depth knowledge of data analytics.<\/span>\u00a0<\/span><\/p>\n Self-service analytics builds on this and enables non-technical users to perform analysis and gain insights independently. This greatly reduces the dependence on \"smart data people,\" allowing them to focus on the more complex issues. For the non-technical user, \"talking\" to reports using Natural Language Querying (NLQ) and automatically visualizing and reporting data will be especially valuable. Through this more intuitive interaction with data, self-service analytics will become an even more valuable tool for decision-making within organizations.<\/span>\u00a0<\/span><\/p>\n Successfully applying new developments requires increased awareness and attention to a number of preconditions. Scoring with data and data applications starts with a clear goal in mind. A data strategy for the targeted development and value creation of these initiatives. Especially since AI goes far beyond an insight or prediction but can lead to far-reaching changes in work processes and the design and staffing of your organization.<\/span>\u00a0<\/span><\/p>\n To create a data strategy, we work from Cmotions with the <\/span>Target to Data model<\/span><\/a> which ensures that the relationship between data and organizational goals is secured and the right priorities are set. The data strategy provides answers to the questions:<\/span>\u00a0<\/span><\/p>\n From the clear starting point of a good data strategy, the next step is to move to implementation, making AI work for your organization. This requires attention to the following:<\/span>\u00a0<\/span><\/p>\n Also, data governance, security and privacy are all by no means \"new kids on the block,\" but their importance has been so evident and growing for years that we thought it worth paying attention to them again. In addition, regulation from the European Union will become active in 2026 for all member states as published in the <\/span>EU AI Act<\/span><\/a>. This may seem like a long way off, but in implementing the AVG legislation, it was clear that <\/span>start on time<\/span><\/a> necessary for timely implementation. The \"holy\" trinity of data governance, security and privacy should help ensure the integrity, confidentiality and ethical treatment of data within organizations. In an era when data is a valuable business asset, upholding these principles is paramount to the success and reputation of organizations.\u00a0<\/span>\u00a0<\/span><\/p>\n The reasons to always be concerned with this trinity are each individually of great importance, but collectively form the basis of a correct, safe and ethical organization:<\/span>\u00a0<\/span><\/p>\n A well-established data governance organization provides clear responsibilities and processes to ensure that there is monitoring and direction on the desired way of working with data.<\/span>\u00a0<\/span><\/p>\n Data literacy refers to people's ability to understand, interpret and use data effectively. This enables organizations to make more informed decisions based on data. Without data literacy, you will never achieve successful self-service analytics. To promote data literacy, it is essential to train employees in interpreting data, understanding graphs and reports, and being able to apply data when making decisions. This can be achieved through training, knowledge sharing and fostering a data-driven culture within an organization. If you are interested in how to entice your employees to a higher level of data literacy, be sure to check out our <\/span>data game<\/span> once, a fun and no-obligation way to let your employees experience how fun and useful data can be for them.<\/span>\u00a0<\/span><\/p>\n In addition to all these fantastic uses of data and AI, we are, unfortunately, all too familiar with its obvious drawbacks. We need only mention the word allowance crisis and everyone will know what is meant. This is a clear example of handing too much over to \"automation\" and too little to humans. Because at the end of the day, AI at this point is still working for and through us and not yet with us. Humans are at the controls and they are needed to keep the human touch. To ensure that data and AI are used in the right ethical way, an organization must get to work developing ethical framework and working on the right <\/span>culture<\/span><\/a> and monitoring thereof:<\/span>\u00a0<\/span><\/p>\n Through clear frameworks and a culture in which data, AI and ethics are equally important, you can help your organization be a \"good\" organization. Employees should not be forced to adhere to requirements, but rather be inspired and put in charge themselves. Ultimately, people are an organization's ethical compass. Together you steer the boat.<\/span>\u00a0<\/span><\/p>\n What we still see a lot is that the legal frameworks are taken as a starting point, but that mainly determines \"what is allowed.\" Ethical frameworks go further, and should mainly determine \"what we do or do not want\" as an organization.<\/span>\u00a0<\/span><\/p>\n If you, like many others, are also struggling with this aspect, keep an eye on our website and socials, because in Q2 we will come up with a series of articles on this topic.<\/span>\u00a0<\/span><\/p>\n For many companies, 2024 is the year of doing, we couldn't really ignore data for a long time, but with the rapid developments in AI, it is increasingly valuable to develop on this. Therefore, the task of 2024 will be to do even more with data, and above all: organize it well. We hope to have given you inspiration for this with this article.\u00a0<\/span>\u00a0<\/span><\/p>\n Are you looking to get started with data or AI, or have you been at this for a long time but are ready for the next step? We would love to help you! Contact us for a free cup of coffee to discuss this together. We love nothing more than talking about data and AI.<\/span>\u00a0<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section].<\/p>","protected":false},"excerpt":{"rendered":" Every year we like to share with you what we see as the most relevant developments in the field of data & analytics. No futuristic trends far removed from our bed, but things that are directly applicable in your daily practice. So that you can start working with this tomorrow if you so desire. That is what is important to us [...]<\/p>","protected":false},"author":2,"featured_media":9809,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"Elk jaar delen we graag met je wat wij zien als de meest relevante ontwikkelingen op het gebied van data & analytics. Geen futuristische ver-van-ons-bed trends, maar juist zaken die direct toepasbaar zijn in jouw dagelijkse praktijk. Zodat jij hier morgen mee aan de slag kan als je zou willen. Dat is wat wij belangrijk vinden bij Cmotions, alle mooie toepassingen van data en AI vertalen naar jouw dagelijkse realiteit, zonder opsmuk en hypes, maar met focus op bruikbaarheid en waarde toevoegen.<\/span>\u00a0<\/span>\r\n\r\nBinnen Cmotions hebben wij aandacht voor de gehele waardeketen van data. Deze keten loopt van \u201cwat willen wij en waarom\u201d (de strategie) naar het ophalen en structureren van data (engineering), tot aan het slim inzetten van data om tot praktisch toepasbare inzichten procesverbeteringen te komen. Daarom zullen de trends die wij bespreken ook vanuit al deze elementen van de waardeketen komen. Focus in de breedte is hoe wij dat graag noemen.<\/span>\u00a0<\/span>\r\n\r\nWe zien voor 2024 twee voornaamste ontwikkelingen: 1) Generative AI en 2) Selfservice & Augmented BI. Zoals je van ons gewend bent vertellen we in duidelijk taal wat dit betekent voor jou, maar niet minder belangrijk, wat ervoor nodig is om deze twee trends succesvol in te zetten.\u00a0<\/span>\u00a0<\/span>\r\n\u00a0<\/h2>\n
1. Generative AI<\/span>\u00a0<\/span><\/h2>\n
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2. Self-service and augmented analytics<\/span>\u00a0<\/span><\/h2>\n
Successfully working with Generative AI and Selfservice\/augmented BI<\/span>\u00a0<\/span><\/h3>\n
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Data governance, security and privacy<\/span>\u00a0<\/span><\/h3>\n
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Data literacy<\/span>\u00a0<\/span><\/h3>\n
Data-driven culture and ethics<\/span>\u00a0<\/span><\/h3>\n
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Bring it on 2024!<\/span>\u00a0<\/span><\/h3>\n
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1. Generative AI<\/span>\u00a0<\/span><\/h2>\r\nOm maar meteen te starten met de olifant in de kamer, op geen enkel trendlijstje van dit moment kan Generative AI uiteraard ontbreken. 2023 was het jaar waarin Generative AI met een big bang naam en faam verwierf bij het grote publiek. Zelfs als je niets met data (te maken) hebt weet je waarschijnlijk wel wat ChatGPT is, om maar \u00e9\u00e9n voorbeeld van Generative AI te noemen. 2024 wordt voor veel bedrijven het jaar om er zelf serieus mee aan de slag te gaan, op zoek naar waardevolle toepassingen. Hierbij staat wat ons betreft gecontroleerd gebruik centraal. We zien heel veel potenti\u00eble waarde, maar erkennen ook de zorgen die geuit worden, zoals in de <\/span>kamerbrief<\/span><\/a> en de <\/span>Rijksvisie AI<\/span><\/a> die eind 2023 en begin 2024 met de Tweede Kamer werden gedeeld.<\/span>\u00a0<\/span>\r\n\r\nVoordat we het in meer detail over Generative AI gaan hebben, willen we eerst even uitleggen wat wij verstaan onder \u201cgeneratieve\u201d artifici\u00eble intelligentie (AI) en wat het verschil is met \u201cnormale\u201d AI. De AI die we al vele jaren inzetten is als een hele slimme collega die goed is in een specifieke taak. Denk bijvoorbeeld aan het voorspellen wanneer een onderdeel van een machine onderhoud nodig heeft, wat vandaag de beste prijs is voor een product op basis van vraag en aanbod of welk deel van het klantenbestand mogelijk zijn contract op wil gaan zeggen. Generative AI is ook een hele slimme collega, maar deze collega kan je op veel meer verschillende taken inzetten, niet alleen omdat deze collega veel meer kennis en \u201cervaring\u201d heeft, maar ook omdat deze ook nieuwe dingen kan bedenken. En die nieuwe, slimme collega spreekt ook nog eens jouw taal \u2013 business taal in plaats van data science vakjargon - en snapt razendsnel wat je bedoelt. Een aantal voor de hand liggende voorbeelden van de inzet van Generative AI:<\/span>\u00a0<\/span>\r\n
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2. Self-service en augmented analytics<\/span>\u00a0<\/span><\/h2>\r\nSelf-service analytics is niet bepaald een nieuwe trend, maar toch eentje die (weer) het benoemen waard is. Na een eerdere grote hype die zichzelf weinig waar heeft gemaakt, brengt met name Augmented analytics de Self-service analytics toch weer dichterbij en daarmee onder de aandacht.\u00a0<\/span>\u00a0<\/span>\r\n\r\nAugmented analytics verwijst naar het gebruik van AI en machine learning om het analytische proces te verbeteren en te automatiseren. Het doel is om analytics toegankelijker te maken voor mensen met verschillende niveaus van expertise en om snellere en beter onderbouwde beslissingen mogelijk te maken. Augmented analytics integreert geavanceerde analyses, voorspellende modellering en natuurlijke taalverwerking om inzichten te genereren, aanbevelingen te doen en patronen te identificeren zonder dat gebruikers diepgaande kennis van data-analyse nodig hebben.<\/span>\u00a0<\/span>\r\n\r\nSelf-service analytics bouwt hierop voort en maakt het voor niet-technische gebruikers mogelijk om zelfstandig analyses uit te voeren en inzichten te verkrijgen. Dit maakt de afhankelijkheid van \u201cslimme data-mensen\" een stuk kleiner, waardoor deze zich op de meer complexe vraagstukken kunnen richten. Voor de niet-technische gebruiker zal vooral het \u201cpraten\u201d met rapportages middels Natural Language Querying (NLQ) en het automatisch visualiseren en rapporteren van data vooral van grote waarde zijn. Door deze intu\u00eftievere interactie met data zal self-service analytics een nog waardevoller instrument worden voor besluitvorming binnen organisaties.<\/span>\u00a0<\/span>\r\n
Succesvol werken met Generative AI en Selfservice\/augmented BI<\/span>\u00a0<\/span><\/h3>\r\nNieuwe ontwikkelingen succesvol toepassen vraagt om verhoogd bewustzijn en aandacht voor een aantal randvoorwaarden. Scoren met data en data toepassingen begint met een helder doel voor ogen. Een datastrategie voor het gericht ontwikkelen en tot waarde laten komen van deze initiatieven. Zeker omdat AI veel verder gaat dan een inzicht of een voorspelling maar tot ingrijpende veranderingen kan leiden in werkprocessen en de inrichting en bezetting van je organisatie.<\/span>\u00a0<\/span>\r\n\r\nVoor het opstellen van een datastrategie werken we vanuit Cmotions met het <\/span>Target to Data model<\/span><\/a> wat ervoor zorgt dat de relatie tussen data en organisatiedoelen geborgd wordt en de juiste prioriteiten gesteld worden. De datastrategie geeft antwoorden op de vragen:<\/span>\u00a0<\/span>\r\n
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