Article | February 17, 2020
The continuous advancements in technology and the increasing use of smart devices are leading tremendous growth in data. Considering reports, more than 2.5 Quintilian bytes of data are generated on a daily basis and it is expected that 1.7 Mb of data will be produced every second in the near future. This is where data scientists play an influential role in analyzing these immense amounts of data to convert into meaningful insights. Data science is an overriding method today that will remain the same for the future. This drives the need for skilled talent across industries to meet the challenges of data analytics and assist delivering innovation in products, services and society.
Article | February 17, 2020
Machine Learning (ML) has taken strides over the past few years, establishing its place in data analytics. In particular, ML has become a cornerstone in data science, alongside data wrangling, and data visualization, among other facets of the field. Yet, we observe many organizations still hesitant when allocating a budget for it in their data pipelines. The data engineer role seems to attract lots of attention, but few companies leverage the machine learning expert/engineer. Could it be that ML can add value to other enterprises too? Let's find out by clarifying certain concepts.
What Machine Learning is
So that we are all on the same page, let's look at a down-to-earth definition of ML that you can include in a company meeting, a report, or even within an email to a colleague who isn't in this field. Investopedia defines ML as "the concept that a computer program can learn and adapt to new data without human intervention." In other words, if your machine (be it a computer, a smartphone, or even a smart device) can learn on its own, using some specialized software, then it's under the ML umbrella. It's important to note that ML is also a stand-alone field of research, predating most AI systems, even if the two are linked, as we'll see later on.
How Machine Learning is different from Statistics
It's also important to note that ML is different from Statistics, even if some people like to view the former as an extension of the latter. However, there is a fundamental difference that most people aren't aware of yet. Namely, ML is data-driven while Statistics is, for the most part, model-driven. This statement means that most Stats-based inferences are made by assuming a particular distribution in the data, or the interactions of different variables, and making predictions based on our mathematical models of these distributions. ML may employ distributions in some niche cases, but for the most part, it looks at data as-is, without making any assumptions about it.
Machine Learning’s role in data science work
Let’s now get to the crux of the matter and explore how ML can be a significant value-add to a data science pipeline. First of all, ML can potentially offer better predictions than most Stats models in terms of accuracy, F1 score, etc. Also, ML can work alongside existing models to form model ensembles that can tackle the problems more effectively. Additionally, if transparency is important to the project stakeholders, there are ML-based options for offering some insight as to what variables are important in the data at hand, for making predictions based on it. Moreover, ML is more parametrized, meaning that you can tweak an ML model more, adapting it to the data you have and ensuring more robustness (i.e., reliability). Finally, you can learn ML without needing a Math degree or any other formal training. The latter, however, may prove useful, if you wish to delve deeper into the topic and develop your own models. This innovation potential is a significant aspect of ML since it's not as easy to develop new models in Stats (unless you are an experienced Statistics researcher) or even in AI. Besides, there are a bunch of various "heuristics" that are part of the ML group of algorithms, facilitating your data science work, regardless of what predictive model you end up using.
Machine Learning and AI
Many people conflate ML with AI these days. This confusion is partly because many ML models involve artificial neural networks (ANNs) which are the most modern manifestation of AI. Also, many AI systems are employed in ML tasks, so they are referred to as ML systems since AI can be a bit generic as a term. However, not all ML algorithms are AI-related, nor are all AI algorithms under the ML umbrella. This distinction is of import because certain limitations of AI systems (e.g., the need for lots and lots of data) don't apply to most ML models, while AI systems tend to be more time-consuming and resource-heavy than the average ML one. There are several ML algorithms you can use without breaking the bank and derive value from your data through them. Then, if you find that you need something better, in terms of accuracy, you can explore AI-based ones. Keep in mind, however, that some ML models (e.g., Decision Trees, Random Forests, etc.) offer some transparency, while the vast majority of AI ones are black boxes.
Learning more about the topic
Naturally, it's hard to do this topic justice in a single article. It is so vast that someone can write a book on it! That's what I've done earlier this year, through the Technics Publications publishing house. You can learn more about this topic via this book, which is titled Julia for Machine Learning(Julia is a modern programming language used in data science, among other fields, and it's popular among various technical professionals). Feel free to check it out and explore how you can use ML in your work. Cheers!
Article | February 17, 2020
One approach for better data utilization is the data fabric, a data management approach that arranges data in a single "fabric" that spans multiple systems and endpoints. The goal of the fabric is to link all data so it can easily be accessed.
"DataOps and data fabric are two different but related things," said Ed Thompson, CTO at Matillion, which provides a cloud data integration platform. "DataOps is about taking practices which are common in modern software development and applying them to data projects. Data fabric is about the type of data landscape that you create and how the tools that you use work together."
Article | February 17, 2020
We currently live in the age of data. It’s not just any kind of data, but big data. The current data sets have become huge, complicated, and quick, making it difficult for traditional business intelligence (BI) solutions to handle. These dated BI solutions are either unable to get the data, deal with the data, or understand the data. It is vital to handle the data aptly since data is everywhere and is being produced constantly.
Your organization needs to discover any hidden insights in your datasets. Going through all the data will be doable with the right tools like machine learning (ML) and augmented analytics.
According to Gartner, augmented analytics is the future of data analytics and defines it as:
“Augmented analytics uses machine learning/artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery, and sharing. It also automates data science and ML model development, management, and deployment.”
Augmented analytics is different from BI tools because ML technologies work behind the scenes continuously to learn and enhance results. Augmented analytics facilitates this process faster to derive insights from large amounts of structured and unstructured data to gain ML-based recommendations. In addition, it helps to find patterns in the data that usually go unnoticed, removes human bias, and allows predictive capabilities to inform an organization of what to do next.
Artificial intelligence has brought about an augmented analytics trend, and there has been a significant increase in the demand for augmented analytics.
Benefits of Augmented Analytics
Organizations now understand the benefits of augmented analytics which has led them to adopt it to deal with the increasing volume of structured and unstructured data. Oracle identified top four reasons organizations are opting for augmented analytics:
Augmented data science availability to everyone has become a possibility thanks to augmented analytics. Augmented analytics solutions come prebuilt with models and algorithms, so data scientists are not needed to do this work. In addition, these augmented analytics models have user-friendly interfaces, making it easier for business users and executives to use them.
You will receive suggestions and recommendations through augmented analytics about which datasets to incorporate in analyses, alert users with dataset upgrades, and recommend new datasets when the results are not what the users expect. With just one click, augmented analytics provides precise forecasts and predictions on historical data.
Natural language processing (NLP) is featured on the augmented analytics platforms enabling non-technical users to question the source data easily. Interpreting the complex data into text with intelligent recommendations is automated by natural language generation (NLG), thus speeding up the analytic insights. Anyone using the tools can find out hidden patterns and predict trends to optimize the time it takes to go from data to insights to decisions using automated recommendations for data improvement and visualization. Non-expert users can use NLP technology to make sense of large amounts of data. Users can ask doubts about data using typical business terms. The software will find and question the correct data, making the results easy to digest using visualization tools or natural language output.
Grow into a Data-driven Company
It is more significant to understand data and business while organizations are rapidly adjusting to changes. Analytics has become more critical to doing everything from understanding sales trends, to segment customers, based on their online behaviors, and predicting how much inventory to hold to strategizing marketing campaigns. Analytics is what makes data a valuable asset.
Essential Capabilities of Augmented Analytics
Augmented analytics reduces the repetitive processes data analysts need to do every time they work with new datasets. It helps to decrease the time it takes to clean data through the ETL process. Augmented analytics allows more time to think about the data implications, discover patterns, auto-generated code, create visualizations, and propose recommendations from the insights it derives.
Augmented analytics considers intents and behaviors and turns them into contextual insights. It presents new directions to look at data and identify patterns and insights companies would have otherwise missed out on completely- thus altering the way analytics is used. The ability to highlight the most relevant hidden insights is a powerful capability.
Augmented analytics, for example, can help users manage the context at the explanatory process stage. It understands the values of data that are associated with or unrelated to that context, which results in powerful and relevant suggestions that are context-aware.
Modern self-service BI tools have a friendly user interface that enables business users with low to no technical skills to derive insights from data in real-time. In addition, these tools can easily handle large datasets from various sources in a quickly and competently.
The insights from augmented analytics tools can tell you what, why, and how something happened. In addition, it can reveal important insights, recommendations, and relationships between data points in real-time and present it to the user in the form of reports in conversational language.
Users can have data queries to get insights through the augmented analytics tools. For example, business users can ask, “How was the company’s performance last year?” or “What was the most profitable quarter of the year?” The systems provide in-depth explanations and recommendations around data insights, clearly understanding the “what” and the “why” of the data.
It enhances efficiency, decision-making, and collaboration between users and encourages data literacy and data democracy throughout an organization.
Augmented Analytics: What’s Next?
Augmented analytics is going to change the way people understand and examine data. It has become a necessity for businesses to survive. It will simplify and speed up the augmented data preparation, cleansing, and standardization of data, thus assist businesses to focus all their efforts on data analysis.
BI and analytics will become an immersive environment with integrations allowing users to interact with their data. New insights and data will be easier to access through various devices and interfaces like mobile phones, virtual assistants, or chatbots. In addition, it will help decision-making by notifying the users of alerts that need immediate attention. This will help businesses to stay updated about any changes happening in real-time.
Frequently Asked Questions
What are the benefits of augmented analytics?
Augmented analytics helps companies become more agile, gain access to analytics, helps users make better, faster, and data-driven decisions, and reduces costs.
How important is augmented analytics?
Augmented analytics build efficiency into the data analysis process, equips businesses and people with tools that can answer data-based questions within seconds, and assist companies in getting ahead of their competitors.
What are the examples of augmented analytics?
Augmented analytics can help retain existing customers, capitalize on customer needs, drive revenue through optimized pricing, and optimize operations in the healthcare sector for better patient outcomes. These are some of the examples of the use of augmented analytics.
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