When approaching BI tools, you may face various buzzwords you have not heard before. This can make the process of implementing these tools feel daunting. However, BDI is here to help all organisations use data intelligently, so we have created this guide to the most important buzzwords that are key to understanding the BI sector.
In this guide, BDI explores the meaning behind the following buzzwords:
Table of Contents
Data accuracy is perhaps the easiest term to guess the meaning of in our list of BI buzzwords, since it refers to records that are reliable and error-free sources. It is a critical component of a data framework, as an organisation cannot make confident and positive decisions without accurate data to base it on.
A KPMG study states that 60% of organisations are not confident in their data and analytics. Furthermore, only 45% of those surveyed reported ‘consistent use of rigorous quality check to ensure the accuracy of data.’ When one considers the importance of accurate data, it is surprising that more organisations are not checking their data accuracy more often.
Data accuracy leads to business success. Fortunately, conducting a data quality audit before hiring a data analyst or using an automated solution can resolve the accuracy gap. Once this gap is resolved, organisations can use data more effectively for better decisions, lower costs, improved marketing processes, improved productivity, and better compliance. Data accuracy is therefore a very important buzzword to understand.
In the simplest terms, this term means analytics to show what could happen.
Predictive analytics is the practice of extracting information from data sets to forecast future probabilities. As a result of analysing current and historical data, organisations can better understand their customers, products, and partners. Predictive analytics use trends to visualise potential situations if a particular factor changed.
The accuracy of predictive analytics depends on the data used to create the model. For instance, more accurate and detailed data leads to more accurate predictions. Predictive analytics creates more sustainable and data-driven decisions for organisations.
It is easy to confuse predictive and prescriptive analytics as the two BI buzzwords have very similar names. In essence, prescriptive analytics refers to analytics that shows what should be done.
Prescriptive analytics builds on predictive analytics, adding analysis and including proposed actions. This methodology attempts to quantify the effect of future decisions on an organisation. As a result, stakeholders can asses and evaluate possible decision outcomes before implementation, allowing confidence.
When prescriptive analytics are properly implemented, they can explain not only what will happen to an organisation but why.
Gartner coined the term ‘X analytics’ to encompass a variety of data format types and allows an organization to extract value from this data without having to analyse the data sets individually. This includes data such as text, video, and audio analytics.
X analytics also opens the opportunity to compare how behaviour has changed and what patterns remain, allowing an organisation to capitalise on them. Furthermore, X analytics can combine with other analytics methodologies such as predictive and/or descriptive analytics for further insights.
In theory, X analytics will play a key role in identifying, predicting, and planning for natural disasters and business opportunities/crises in the future, according to Gartner.
It is important to note that the models to allow this have not been fully developed yet. Whilst the principle is there, and some models can be used efficiently, the models are not prevalent enough for widespread use. However, it is certainly an interesting section of BI buzzwords to watch for in the future.
A growing and exciting area of technology, decision intelligence is a buzzword that refers to a combination of machine learning and artificial intelligence. It allows users to make more intelligent decisions through machine learning algorithms providing relevant data.
Decision intelligence has three types of models: human-based, machine-based, and hybrid. Each model refers to the level of human involvement with the analytics process.
One key benefit of decision intelligence is the opportunity for consistent approaches to challenges. For example, banking staff can make fairer decisions through removing human internal bias and emotional influence and presenting concrete decision intelligence data and risk predictions.
Cognitive computing enables the digestion of massive volumes of structured and unstructured data into manageable content.
The technology is designed to mimic the human brain – hence the name ‘cognitive’. Through developments in cognitive computing technologies, engineers and programmers hope to replicate human information processing abilities. BI software can then apply this software, thus identifying consumer behaviours, patterns, and trends, for more accurate decisions. This next-generation system has applications across a variety of industries, ranging from healthcare to e-commerce.
Examples of cognitive computing include machine learning, pattern recognition, and natural language processing.
Natural Language Processing (NLP)
As mentioned above, Natural Language Processing (or NLP) is classed within the umbrella category of cognitive computing. NLP refers to a development in artificial intelligence solutions designed to make human-computer interaction easier and more efficient.
NLP helps users by revealing text patterns which may otherwise go unnoticed. They allow relatively non-technical users to perform complex analysis of human text without the intervention of an IT team. Experts predict this technology will become even more accessible to non-technical end-users in the future as the tools continue to develop.
Some simple examples of NLP tools include virtual assistants such as Siri and Alexa, machine translation, autocorrect tools, and bots. The tools can also be used within BI software for opinion mining, such as social media monitoring. These tools allow organisations to understand audience sentiment, which in turn can guide future strategies.
The term ‘mobile analytics’ refers to the ability to access analytics software on mobile devices, such as smartphones and tablets. As a result, it is an increasingly popular feature within BI tools.
A key benefit of mobile analytics is that they allow quick and easy access to data for key stakeholders on-the-go. Rather than having to wait for a specific team member to be in the office to make a decision, that team member can access data from any location via their mobile analytics option. This adds value to a business as a result. Mobile analytics is secure, often requiring two-factor authentication for a user to access data on a mobile device.
BDI offers a range of mobile-optimised data analysis and reporting tools, and our team of data experts can help set them up so you can access your important data from any location.
Self-service BI is an automated form of business intelligence tools that help make it accessible to anyone and everyone, rather than purely intelligence analyst experts. Even relatively non-technical end-users should be able to access data through self-service BI tools.
One outcome of self-service BI is the creation of a streamlined process that helps increase productivity and overcome skills gaps within an organisation. Users can independently access self-service BI without the need for assistance from an IT team, after some initial training. Correspondingly, decision-making processes are faster, reducing the chain of people needed to get from point A (finding data) to point B (applying the data to a decision).
Linked to self-service BI, digital automation refers to the technologies that provide automated processes to make big data and analytical analysis easier to understand and use. Business functions, such as repetitive human admin tasks or certain decision-making tasks, can be automated to provide a more efficient workforce who are not slowed down by menial tasks.
Many different industries use these tools, helping increase productivity and solve business challenges through digital transformation. Correct data can be accessed easily and quickly, helping inform important business practices.
Digital automation platforms are often implemented as part of digital transformation initiatives, which involves large-scale efforts to convert to digital technologies, business practices, and a digital culture. It is important to reiterate, however, that digital automation refers specifically to the automation of human tasks.
The main ROI from digital automation includes:
Keep up with BI buzzwords and industry news
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Do you think one of the BI buzzwords included in this article could help your organisation? One of our business experts would welcome the opportunity to talk through your needs.
BDI has developed a range of practical, ready-to-deploy modules to extend and improve our customers’ existing software. SunSystems and Infor Q&A users will be particularly interested in our web-based Budgeting & Forecasting, Financial Reporting, and KPI style Dashboard packages. These also extend to companion products, like iPOS, and other ERP systems.
If you’re a SunSystems of Infor Q&A user, please contact Nick or one of our sales team for further insight. We offer Business Systems Reviews, Budgeting and Forecasting, and Data Analysis and Review services, personalised to each organisation. To find more information about these services, please visit the ‘Services’ page or get in touch with one of our team. We look forward to working with your organisation soon.