Big data, AI, real-time, predictive or machine learning - when you move around in the field of BI and Analytics, one thing quickly becomes clear: buzzwords as far as the eye can see. Who can keep track of the big picture? Of course, even though these technologies and concepts all actually exist, you often get the impression that the naming of specific products is becoming more and more arbitrary. For companies, this means difficult conditions when it comes to navigating the jungle of products around data and company. On closer inspection, however, it becomes clear that behind all the colorfully mixed wishes and associations, there is one and the same vision - driven by data, to act as goal-oriented as possible in order to achieve the best possible results.
Those who want to be competitive in the future with increasing digitization and internationalization and who want to achieve economic success must think "data-driven". Employees from all areas - whether in the head office or in the field, whether with a strategic or operational focus, whether Category Management or CRM - are thus enabled to make their daily decisions better and faster based on data, and to perfect processes with the support of data. This vision does not have to be a dream - with years of experience in Business Intelligence (BI) and best practices from the market, minubo has developed a blueprint for successful BI projects in retail that works completely independent of any solution. A total of 7 Success Factors are presented below.
Review: The Classic BI
Collect, Integrate, Visualize - three keywords that describe a very simple project approach that was widespread in the traditional BI world. In essence, classic BI meant that IT constructed an infrastructure that collected data from multiple sources, integrated it into a data warehouse, and were (at best) visualized with a generic visualization tool, such as tableau or Power BI. The fact that this approach is reaching its limits today for various reasons is probably not a big surprise:
On one hand, traditional BI systems are often developed with a strong IT-centric view. They are based on the available data and the technological foundation, rather than the use cases and questions of the users. It is hardly considered who, in what form and for what the data is needed. This usually results in a very poor user orientation of the solution. On the other hand, a potential self-service of users fails in many cases due to the generalist approach of the tools provided. Due to the poor user-friendliness, as well as the often missing communication and involvement of the employees in the process of the solution development, there is often hardly any use at the end of the day.
Also, on the part of technology, two aspects in the classic BI world are regularly underestimated: the data modeling and scalability, and the long-term performance of the solution. Building a logical and (in the long term!) fast data warehouse is, however, a prerequisite for being able to keep up with the fast-moving market. It also requires a high level of agility and flexibility in order to be able to continuously adapt to the changing conditions - a fact that is also rare in the traditional BI environment.
So, if classic BI cannot meet today's requirements, which aspects will determine the success or failure of your BI project?
minubo has taken this question as an incentive to develop a guide to successful value creation from data: 7 Success Factors are summarized in a Commerce Intelligence Blueprint, which is broken down at the project and solution level. To be clear: Commerce Intelligence is the term for Business Intelligence, geared specifically to the needs of the commerce industry.
The commerce intelligence blueprint
The project level
In order to establish a successful BI solution in a company, not only do technical criteria have to be fulfilled, but the implementation of the project also determines the success or failure. Concretely, three factors play a decisive role: interdisciplinarity, agility and organization.
Success Factor 1: Interdisciplinarity
True value creation from data only succeeds if use cases and user needs are at the center of consideration. Therefore, it is crucial that BI projects follow an interdisciplinary approach and involve representatives from all areas of the business - under a business and IT owner team. This ensures that from the beginning all prerequisites are taken into account, which later are crucial for an organization-wide, data-driven work culture.
Success Factor 2: Agility
While in the classic BI world, a traditional, simple project approach according to the scheme "collect requirements - implement solution - roll out solution" was widespread, it is now more important than ever to be able to act quickly. The proliferation of agile methods proves to be expedient here as well. After the interdisciplinary collection of use cases, they are prioritized and successively rolled out in an iterative process. The most important areas of application are thus quickly productive. It also creates room for testing and optimizing selected methods and an agile technological setup in a production environment to ensure a sustainable value-adding solution.
Success Factor 3: Organization
In order to successfully establish a data-driven work culture, not only is the participation of all subsequent users in the requirements analysis important, but also the involvement of the individual users in the overall process as well as the communication of goals and added value play a crucial role: Why is data-driven work important? Why do we expect a high added value for our company? How does working with data help you personally? The entire change management process required here needs a dedicated person in charge. He or she should pursue the primary goal of designing and establishing the new work processes together with the users, as well as culturally anchoring the possibly new data-driven mindset throughout the organization.
The Solution Level
At the solution level, four other key factors are highlighted that allow you to effectively derive real value from your data: Concept, Democracy, Relevance, and Convenience.
Success Factor 4: Concept
This term brings together many of the elements that have been the focus of attention in the traditional BI world, even though they are not necessarily the "visible" part of the BI solution for the user. For example, data collection and integration, which, of course, has an essential meaning in any form of BI, modern or traditional. However, there are also two other fundamental factors to consider: on the one hand, the concrete definition of the data model - the key figures and dimensions and the logic that systematically link these together - and on the other hand the guarantee of long-term scalability. Due to the steadily increasing amounts of data and in order to avoid a subsequent cost explosion, care must be taken at an early stage of a long-term scalable infrastructure, database technology and query methods.
Success Factor 5: Democracy
BI today has to be more than just a system from which IT researchers and analysts extract data for business stakeholders on demand - the requirements are too diverse and practical, and the speed at which the information is needed is too high. It is therefore crucial that all stakeholder groups have self-service access to the data and data tools that are relevant to them: not only stakeholders from the areas of business analytics and controlling, but also stakeholders with a strategic focus (department management, management, and possibly holding companies or investors) as well as stakeholders from all operational areas (from category management to CRM, at the desk and in the field).
Success Factor 6: Relevance
Not only does relevance mean delivering "correct numbers" from a huge data warehouse, it also means that through the method of data preparation, use-case-specific cognitive values are ideally served on a silver platter. This requires tools with so much specialist know-how that they provide each role with exactly the insights that are important for solving their individual challenges. For strategic stakeholders, for example, these are primarily tools that enable a quick, easy overview – for example, simple dashboarding or clickable monthly reports on their own business area. For analytical stakeholders, on the other hand, flexible tools are usually more important for complex ad-hoc analyses at the raw data level. In turn, operational user groups focus on actionability and process orientation.
Success Factor 7: Comfort
Last but not least - comfort. Real value creation from data can only succeed if working with data becomes a natural part of the daily work processes of all users. Easy usage is therefore the prerequisite to enable true self-service. In addition, proactivity and automation are other important keywords: If information has to be compiled through elaborate analyses, the good intentions may be quickly thrown overboard again. Automated alerts that proactively inform about changes in relevant business metrics help deliver critical insights and help you respond to events in a timely manner. The same applies to the subject of automation: The ability to build intelligent customer segments in a BI solution does not necessarily mean that the user implements this as well. On the other hand, if the segments are automatically transferred to a campaign tool, the motivation to exploit the potential of the BI database increases.
So that's what they are, the seven success factors for a BI project: interdisciplinarity, agility and organization at the project level as well as concept, democracy, relevance and comfort at the solution level. If you can bring the BI blueprint to life for your own business with a clear vision, then it's time to move back into the world of buzzwords: does "comfort" in my case mean artificial intelligence, which proactively makes recommendations to employees? In order to improve the attribution of campaign touchpoints, will I, for the relevance aspect, incorporate a machine learning unit into my marketing analytics in a later evolutionary stage? Does it make sense for a particular group of users to provide certain data points in real-time? This clear vision and an orderly concept of BI should help to navigate the buzzword jungle and to identify the necessary basics and areas of application.
However, one challenge remains: the implementation - especially at the conceptual and technical level. Are there resources, expertise, time and money to develop a solution yourself? If so, this can be a valuable process that generates helpful experiences and leads to a tailor-made solution. If this is not the case, the market offers opportunities to buy all or part of the solutions. An out-of-the-box solution from the commerce area that follows the blueprint in all its aspects is minubo. Data from fragmented system landscapes are combined, enabling true omni-channel commerce after only a short implementation time. With this flexible end-to-end solution, people in strategic, operational, and analytic roles make the right, data-driven decisions every day, implementing better, data-driven processes - the best foundation for true value creation and a significant competitive advantage.
About minubo GmbH
Head office: Hamburg
Mangement: Lennard Stoever
Branch: Enterprise Business Intelligence
Customer: B2B und B2C