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Everyone is prey to cognitive biases that skew thinking, but data scientists must prevent them from spoiling their work. Learn more about five biases that can all too easily make your seemingly objective work become surprisingly subjective.

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There are many reasons why data scientists should learn Java. Read this overview of 6 specific reasons to help decide if Java might be right for your projects.

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Few years ago, analyzing data in the petabyte range required a lot of work in the command line. R, Python, Gnuplot and SQL were vital in achieving the desired analysis. Then Tableau came and analysis were carried out without writing scripts. From then on, analyzes were interactive and the data strategy has changed fundamentally.

A lot has happened since then, not only in research. Business intelligence has also undergone profound changes in recent years. In 2015, cloud analytics, data science and the connection of big data were still in the foreground, so in 2016 self-service analyzes clearly came into focus. More companies are giving their employees access to their data. More people understand data as an important tool for performing their tasks. Here, we will discuss the BI & Analytics trends shouldn’t be missing in any data strategy in 2020.

1. Everyone can use the “modern BI”

Modern BI is a model of business intelligence that makes data accessible to more employees in various roles. This aspect is also mentioned in the BI Magic Quadrant from Gartner. It says that “we have passed the decisive turning point of a more than 10 to 11-year transition from IT-centric reporting platforms to modern BI and analysis platforms”. This is particularly important for companies that may have terabytes or more of data and need to ensure that users perform their analysis with clean and IT-approved data.

2. Analyzes are becoming more collaborative

We will see a change in collaboration in 2020. Instead of forwarding static reports, users will share interactive workbooks and data sources that serve as the basis for their business decisions. For example, imagine that during a weekly business meeting, you call up an interactive dashboard to check KPIs. It will also be quite common to carry out analyzes in these dashboards directly from the browser or the iPad.

3. All data are given equal rights

In 2020, the value of the data will no longer be tied to rank or size. Loading a database with billions of rows should work just like loading an Excel spreadsheet with 150 rows from your desktop. It will be important that employees can access data quickly and easily and can examine it together with other data types.

4. Self-service is extended to data preparation

The trend towards usability and agility that has revolutionized the BI and analytics markets is now reaching data preparation. This means that common tasks such as syntactical analysis, JSON and HTML imports and data processing are no longer delegated to specialists. Instead, non-analysts will be able to perform these tasks as part of their analysis flow.

5. Working with data without knowing it

Not surprisingly, analytics work best when it’s a natural part of the workflow. In 2020, analyzes will be omnipresent and enrich all business processes.

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Embedded BI will increase the scope of the analyzes to such an extent that this development may not be perceived consciously. It’s similar with predictive analysis that recommends a movie on Netflix. These are the results of analyzes. Most people are not even aware of this.

6. IT becomes a data hero

For decades, IT departments have struggled to create endless reports to answer business requests. This cycle is now interrupted. IT departments no longer produce, but support and ensure governance, data security and compliance. IT enables the company to make data-oriented decisions with the speed required by the market. In a way, IT becomes a data hero.

7. Employees work with data in a more natural way

Writing SQL is not a very natural way to work with data. In 2020, the user interface for working with data will become even more natural, through natural language. Natural language analysis means that data questions are formulated with common words. This makes data, graphics and dashboards even more accessible by giving employees the opportunity to interact with data in new ways.

8. The transition to the cloud is accelerating

Data gravity is the idea that we want to do the analysis where the data is. So if your data is stored in the cloud, we also want to carry out the analyzes there. In 2020, data in the cloud will develop enough “gravity” to persuade companies to provide their analysis where the data is. Cloud data warehouses like Amazon Redshift will remain very popular data locations and as a result, cloud analytics will be ubiquitous.

9. Advanced Analytics becomes more accessible

Not every user can program R or Python. Business users in particular will not want to acquire this knowledge and avoid analysis functions that require such scripting languages. In 2020, advanced analytics (sophisticated, powerful analytics) will be more accessible and available to business users for everyday use.

10. Data and analysis competence is the focus

There is no profession that can do without data today. This means that data and analysis skills will become increasingly important — regardless of the role and position in the company. For two consecutive years, this skill has been listed as the main recruitment requirement for LinkedIn. Intuitive analytics platforms are introduced at the workplace, providing decision-making bases at all levels. But the skills of the employees form the foundation for using them.

Author Bio:

Melissa Crooks is Content Writer who writes for Hyperlink InfoSystem, a mobile app development company in New York, USA and India thatholds the best team of skilled and top app developers. She is a versatile tech writer and loves exploring latest technology trends,entrepreneur and startup column. She also writes for top app development companies.

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Essential Business Intelligence And Analytics Trends was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Embracing Simplicity

Think of simplicity as of a budget. Whenever an engineer decides to work on some aspect of the software they own, they effectively decide to spend some of that budget.

Unlike well-known error budgets, simplicity budgets do not get reset over time. While it is possible to increase the remaining budget by regaining some simplicity, one will have to pay “a tax” as moving from simple to complex is much easier than moving in the opposite direction.

Simple is better than complex.
Complex is better than complicated.
– Tim Peters, The Zen of Python

An exhaustion of simplicity budget indicates that the system has become unmaintainable. Not only does it mean that any future changes to the system are likely to cause a decline in its essential metrics, but it also dramatically reduces the team velocity.

On simplicity, reliability and safety

As engineers, we may often find ourselves drowning in complexity. Let’s try to reflect on how we ended up there. One of the aspects that might catch our attention could be for instance how ridiculously paranoid a codebase is when it comes to failures. It practically screams, “No, you are not allowed to fail!”, and yet the engineers have been complaining about how unreliable our tools are.

That is our “Aha!” moment. We’ve been trying so hard to make our tools safe that we’ve simply exhausted our simplicity budget. Apparently, we also fell victim to a post hoc fallacy of expecting the reliability of the system to improve as a result of improving its safety.

Think of a Swiss Army pocket knife. It takes just a few minutes to cut our finger while simply exploring its features. Was that knife safe? Not at all. Was it reliable? Yes, indeed it was. Simply put, safety and reliability are independent concepts.

Simplicity is prerequisite for reliability.
– Edsger W. Dijkstra

In the long run, it is the exhaustion of simplicity budget that has a dramatic impact on reliability of our systems, not a lack of safety.

Embracing simplicity

Why have some platform core Utilities hardly change over the course of their existence? Maintainers are extremely cautious about spending their simplicity budgets and tried very hard to find an optimal balance.

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Consider a platform: it is foolproof enough to prevent you from purging the root of your filesystem, but it will allow you to delete pretty much everything else you can access without turning a hair.

So, what is to be done? First of all, embrace simplicity.

Manifest plainness. Embrace simplicity. Put others first. Desire little.
– Lao Tzu, Tao Te Ching

Remember that simplicity decreases over time, and regaining it is a slow and painful process. When working on software, spend the simplicity budget wisely.

Second, do not be afraid to favor simplicity over other aspects of the software. While it feels natural to automate things at a platform scale, always ask yourself if, say, another safety feature is worth spending the simplicity budget on. After all, it’s through failure that we learn the greatest lessons that life or job could teach us.

Last but not least, make sure all the stakeholders are on the same page when it comes to the previous items and decision making. Different problems naturally imply different trade-offs, and those should be agreed upon in advance.

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Embracing Simplicity in Engineering was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

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