Today, business intelligence solutions have a very important and popular place for companies to make healthy decisions for the future and get ahead of the analytical competition. A deep discovery of data, together with smart technologies, helps companies quickly take their business to the next level and shape their future in a robust way. Marketing campaigns, consumer loyalty and retention, new financial opportunities, and operational efficiency are just some of the benefits of BI. Trending applications in business intelligence are shaped to maximize these benefits. With the spread of trends, it is clear that the time and effort between data and decision-making will be significantly shorter.
The goal is for machines to automate decision-making and for humans to understand their data more accurately and faster. Artificial intelligence (AI) and machine learning (ML) are not yet widespread enough in business intelligence. This is because the answers given by artificial intelligence are not clearly explained. For these applications to become widespread, they need to be transparent and reliable. Over time, many data science tasks will be automated. Machine learning will help skilled analysts find insights that even they struggle to find.
Soon AI will be able to recognize when there is an unusual or significant change in data patterns, regardless of the size and complexity of data sets, and businesses will only need to input raw data. Many organizations have embraced the value of AI and machine learning.
Natural language processing (NLP) combines computer science and linguistics to help computers understand the meaning behind human language, enabling people to converse with data. So a human can ask a BI tool to “find the big earthquakes near Istanbul” or “find the one near Muğla” and get the answers based on data visualization. In modern business intelligence, machine learning enables systems to have deeper knowledge over time, based on a company’s data and the type of questions users ask.
Augmented analytics is a form of data discovery that automates data insight using machine learning (ML) and natural language processing (NLP) – including NLU (natural language understanding) and NLG (natural language generation), which are subdivisions of NLP – to improve data analytics, data sharing and business intelligence. With increasing raw data, the preparation, classification and analysis of data will become automated. The aim is to make less biased decisions with augmented analytics and to engage users more with the data.
Everyone who works with data will eventually be equipped with “voice-activated” digital assistants created with the help of artificial intelligence and natural language processing. Voice-activated virtual assistants such as Siri and Alexa will begin to translate language into text and turn it into structured data to be analyzed for insights. A significant increase in the use of smart assistants is expected in the coming years.
BI platforms unify business processes and workflow with mobile analytics, embedded analytics, and dashboard extensions, ensuring that everyone who works with data stays in the process and in the flow. Actionable analytics accelerate decision-making for all roles, technical and non-technical. This allows those working with data to act quickly after analyzing data and discovering insights.
Embedded analytics makes analytics and insights more accessible to the user, so users don’t need to navigate to another application or a shared server. Mobile analytics brings these capabilities to all users, regardless of where they are physically located. Analytics can only become actionable if the right message is delivered to the right person at the right time.
Data management is the practice of organizing and maintaining data processes that involve acquiring, validating, storing, protecting, and processing the necessary data. As data sources are increasing in number and becoming more and more complex, data management within the framework of business intelligence is becoming very critical. Proper data management ensures data security, eliminates data quality problems, supports risk and compliance processes and increases operational efficiency. Thus, it is of great importance for companies to manage their data effectively and in accordance with corporate data models in order to ensure their sustainability.
Thanks to predictive analytics, we can have an idea about where an industry will head with data such as “what people buy, what they respond to, what they like or dislike”, or a cyber security expert can use predictive analytics to detect fraud or cyber attacks while they are still in the early stages. With predictive modeling, you can accurately perform your risk assessment analysis. Thus, you get reliable predictions in future scenarios.
We know that the world’s leading brands such as Facebook and Google make money from the data they collect from users. When we evaluate business intelligence trends after 2020, it can be predicted that companies will use their data to identify new revenue opportunities. Thus, regulated data trading may be the profession of the future.
Data storytelling is the combination of actions taken to discover and share the important points in the data. Storytelling has become a critical part of the analytical process as it enables people to join the analytical conversation by truly understanding the data. This also supports data literacy efforts. In addition, narrativizing the data makes it easier for analysts to get closer to the result and ensures that the message is communicated in the most effective way. In a conversation that starts around data, the audience is at the center of the conversation. This is why data storytelling has become the new communication language of companies.
Data will move to the cloud at an increasing rate, causing companies to rethink their data strategy. Storing data in the cloud provides companies with many advantages such as low cost, ease of access, security, flexibility and scalability. In addition, the cloud provides the right resource by enabling secure dashboard sharing with partners or customers. Many companies around the world are experimenting with hybrid solutions (private cloud + public cloud) to take advantage of the benefits of various data sources. In the coming days, we will observe that multi-cloud strategies will become dominant.
The benefits of business intelligence in optimizing time and driving business are indisputable. This is why business intelligence is rapidly taking over the world. In order for your company strategies to succeed in a competitive environment, you need to regularly follow the trends in business intelligence and incorporate new business intelligence solutions into your life. Remember that data never lies. Therefore, it is inevitable to go to a solid solution with the insights you reach. By being aware of all these, businesses can easily improve their business processes and workflows.
Do you need some help about your business intelligent project? Contact Enkronos team today.