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Connect with other users of Alteryx products in Istanbul. By joining this group, you gain access to those who regularly work with this software, pose questions, and discuss and share best practices and challenges.
Publisihed here; https://www.meetup.com/tr-TR/Alteryx-User-Group-Istanbul/messages/boards/thread/52441231
Sticking to Waterfall Methodologies in Analytics Projects Many organizations adopt the waterfall methodology while they’re democratizing analytics and BI. That is usually a mistake. Waterfall development is one of the critical reasons why tasks take too long and fall short of providing value. Also, longer waterfall cycles carry the risk of transitioning into bad code and application design mistakes that remain undiscovered until the project’s end.
Agile methodology offers a more viable solution. Agile development methods include shorter, incremental cycles, which allow malfunctions to happen first so that mistakes can be identified and fixed more quickly.
Agile development methods can be an ideal fit for many business applications where continuous refinements are expected. They’re particularly handy where applications need to consider new requirements and new data. If your analytics’ projects are moving slowly, you should analyze whether the traditional waterfall approach is the cause. The next step is to consider whether agile might be the right fit for your goals to expand analytics and BI throughout your organization. Ref: https://heap.io/blog/...
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Self-service analytics will never make their way into the daily workflow throughout the organization if decision makers can’t access them from the applications they’re already using. Fortunately, self-service analytics can integrate directly with technologies like
BI tools; Tableau, PowerBI, Qlik
CRM systems; Salesforce, MD Dynamics
Enterprise data platforms; Siebel, SAP, Netsuite
Mobile Applications ;Web apps, Iso Android Apps etc.
If more analysis is needed, users can gain further insights by drilling down at a granular level in the analytics tool itself. Check out Alteryx Server specifically designed for this purpose Ref: https://heap.io/blog/...
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Puclished here; https://www.meetup.com/tr-TR/Alteryx-User-Group-Istanbul/messages/boards/thread/52441218
Data governance is essential for balancing;
your organization’s need for analytics access and IT’s requirement of maintaining appropriate security.
Some corporations keep all data points under lock and key.
Other companies have set up in a way that completely neglects the need to supervise usage.
Both are the two stupid things that are pretty common. Having everything under lock and key is like governing North Korea.
Users will need to pull and analyze their data from any source but they will be forced to fill in tickets and wait for approvals. This will slow down your analytics processes.
When users want to fuse different datasets in order to uncover new insights, IT will become a chinese wall and you will not be able to benefit from data democracy essential for innovation.
Providing a decentralized, free or "open buffet" environment is not wise either.
With several datasets created from differing sources floating around, it becomes challenging to figure out which one is the single version of the truth.
Also bear in mind that loss or theft of personal data from your company may result in painful fines (GDPR) like just happened to Facebook.
Here is a great read on the topic from the analytics magazine; http://analytics-magazine.org/rise-self-service-analytics/
Cultivating an environment that allows for data governance while encouraging self-service in a centralized manner can address many of the woes mentioned above. As you deploy and configure your self-service tools, make sure to establish the right auditing measures and necessary controls that provide users with data access, but also allows IT to gain the transparency they need to understand who uses what data.
For a solution from Alteryx regarding this please check out Connect; Ref: https://heap.io/blog/...
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Era One: Artisanal Analytics
...data analytics commenced in earnest with what he dubs “artisanal analytics” in 1975.
This methodology was primarily geared towards producing insights for internal decision-making using small-scale, structured datasets.
Era Two: Big Data Analytics
As Silicon Valley began to boom in the late ’90s and early 2000s, the volume and variety of data available for the analyst ballooned.
With the new challenges that this changing landscape provided, a new title entered the lexicon: the data scientist.
Era Three: Data Economy Analytics
Before and around 2013, another major change occurred. As massive tech firms found new ways of wrangling their outsized datasets, they also found new ways of commoditizing them. In addition to building products around the datasets they maintained — the model companies had used for nearly forty years, in Davenport’s framing — they also began to sell the data they were collecting from users.
Era Four: Autonomous Analytics
we have just entered a new era of analytics in the past year, characterized by an even stronger role for autonomous decision-making — probably the loosest definition of artificial intelligence. In this model, machines not only perform the analysis; they also act on the insights, making decisions faster and more efficiently than any human could.
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