Schlagwort: Data Strategy

Newsletter: Data & AI Digest #1

Generated with DALL-E
Generated with DALL-E

Hello and welcome to the first issue of Data & AI Digest! We’ve curated an exceptional list of articles that delve into a wide array of topics—from the triumphs of data visualization in public health to the ethical dilemmas surrounding AI-generated images. Are you curious about running Python directly in Excel? We’ve got you covered!

  • [dataviz] The Triumph Over Tobacco: A Public Health Milestone: Explore how a blend of regulation, taxation, and education led to a significant decline in cigarette sales and lung cancer deaths in the U.S. Read more
  • [dataviz] Master the Art of Data Visualization with These Must-Read Books: Whether you’re a novice or a pro, discover five essential books that guide you to data visualization mastery. Read more
  • [analytics] Python Meets Excel: A New Era in Data Analysis: Announcing Python support in Microsoft Excel, enabling data analysis directly within the Excel grid—no separate Python installation needed. Read more
  • [powerbi] Unlock Power BI’s Full Potential with DAX: Discover 20 essential DAX tricks to enhance your Power BI reports, suitable for both beginners and experts. Read more
  • [ethics] Controversy Over ‚SmashOrPassAI‘ Site: A new site that allows users to rate AI-generated women sparks backlash, raising ethical concerns. Read more
  • [public opinion] Rising Concerns Over AI’s Role in Daily Life: A new Pew Research survey reveals growing apprehension among Americans about the role of AI in daily life, with views varying by age and use cases. Read more
  • [privacy] GDPR Complaint Against OpenAI Over ChatGPT: OpenAI faces allegations of GDPR violations regarding its ChatGPT model, as filed by a privacy researcher. Read more
  • [coding] Meet Code Llama: A New Large Language Model for Coding: Meta is introducing Code Llama, a state-of-the-art large language model designed to assist with coding tasks. Read more

We hope you find this week’s digest both informative and inspiring. Enjoy the newsletter? Help us make it bigger and better by sharing it with colleagues and friends. For even more curated content, discussions, and networking opportunities, don’t forget to join our LinkedIn Data & AI User Group.

Get the Data & AI Digest newsletter delivered to your email weekly.

Stay curious and keep innovating!

Introducing „Data & AI Digest“: Your Essential AI Newsletter for Weekly Insights on Data Science and Artificial Intelligence

Data & AI Digest - the AI newsletter (image generated with DALL-E)
Data & AI Digest – the AI newsletter (image generated with DALL-E)

Why You Can’t Afford to Miss the „Data & AI Digest“

In today’s fast-paced digital landscape, keeping up with the ever-changing worlds of Data Science and Artificial Intelligence (AI) can be challenging. From breakthroughs in machine learning algorithms to ethical debates surrounding AI, the scope of what you need to know is vast and continuously expanding.

Enter the Data & AI Digest, a weekly newsletter curated to keep you updated on the most pertinent news, articles, and discussions in the realms of data and AI. Whether you are a seasoned professional, a student, or simply an enthusiast, this newsletter is designed with you in mind.

„Introducing „Data & AI Digest“: Your Essential AI Newsletter for Weekly Insights on Data Science and Artificial Intelligence“ weiterlesen

9 Key Elements of a Successful Data Strategy for Business Growth

Get the Competitive Edge with Decisively Digital - The Ultimate Guide to Data Strategy
Get the Competitive Edge with Decisively Digital – The Ultimate Guide to Data Strategy

Data is a valuable asset that can give businesses a competitive edge and drive growth in today’s digital age. But without a clear and well-defined data strategy, companies risk missing out on the benefits that data provides. To help your business succeed in the digital world, here’s an overview of nine essential elements of a comprehensive data strategy.

    1. Goals and Objectives: Define specific goals and objectives that the company wants to achieve through its data efforts, such as improving customer experiences or optimizing business processes.
    2. Data Sources: Identify the most valuable data types and determine where they will come from, such as internal transaction or customer data and external market research.
    3. Data Management and Storage: Outline how data will be collected, organized, and stored consistently, accurately, and compliantly, with data management tools and technologies.
    4. Data Analysis and Reporting: Define how data will be analyzed and used to inform business decisions, with data visualization tools, dashboards, and reporting systems.
    5. Data Governance: Establish clear roles and responsibilities for data management, guidelines for data use and access, and ensure ethical and regulatory compliance.
    6. Data-driven Culture: Foster a data-driven culture by providing training and resources for data-driven decision making.
    7. Data Security and Privacy: Ensure data is collected, stored, and used securely and in compliance with privacy regulations.
    8. Data Integration and Interoperability: Define how data will be integrated and shared across systems and platforms.
    9. Data Quality and Accuracy: Ensure data is accurate and up-to-date, with processes for data cleansing and enrichment.

A data strategy is a must-have tool for any company that wants to fully realize the benefits of its data. It provides a clear roadmap for data collection, management, and analysis and helps organizations make better use of their data, drive growth, and succeed in today’s digital world. Get more insights and in-depth information by reading the book Decisively Digital (on Amazon).

Data Operations: Wie Sie die Performance Ihrer Datenanalyse und Dashboards steigern

#dataops: Folgen Sie der Diskussion auf Twitter
#dataops: Folgen Sie der Diskussion auf Twitter

Sind Sie mit der Geschwindigkeit Ihrer Datenanlyse unzufrieden? Oder haben Ihre Dashboards lange Ladezeiten? Dann können Sie bzw. Ihr Datenbank-Administrator folgenden Hinweisen nachgehen, die sich je nach Datenquelle unterscheiden können.

Allgemeine Empfehlungen zur Performance-Optimierung

Möchten Sie die Geschwindigkeit der Analyse verbessern? Dann beachten Sie folgende Punkte:

  • Benutzen Sie mehrere »kleinere« Datenquellen für individuelle Fragestellungen anstatt einer einzigen Datenquelle, die alle Fragestellungen abdecken soll.
  • Verzichten Sie auf nicht notwendige Verknüpfungen.
  • Aktivieren Sie in Tableau die Option »Referentielle Integrität voraussetzen« im »Daten«-Menü (siehe Abbildung 2.20). Wenn Sie diese Option verwenden, schließt Tableau die verknüpften Tabellen nur dann in die Datenabfrage ein, wenn sie explizit in der Ansicht verwendet wird*. Wenn Ihre Daten nicht über referentielle Integrität verfügen, sind die Abfrageergebnisse möglicherweise ungenau.

Aktivierte Option „Referentielle Integrität voraussetzen“ im „Daten“-Menü
Abbildung 2.20: Aktivierte Option »Referentielle Integrität voraussetzen« im »Daten«-Menü

* So wird beispielsweise der Umsatz anstatt mit der SQL-Abfrage SELECT SUM([Sales Amount]) FROM [Sales] S INNER JOIN [Product Catalog] P ON S.ProductID = P.ProductID lediglich mit der SQL-Abfrage SELECT SUM([Sales Amount]) FROM [Sales] ermittelt.

Empfehlungen für Performance-Optimierung bei Dateien und Cloud-Diensten

Achten Sie insbesondere beim Arbeiten mit Dateiformaten, wie Excel-, PDF- oder Textdateien, oder Daten aus Cloud-Diensten wie Google Tabellen zusätzlich auf folgende Punkte:

  • Verzichten Sie auf Vereinigungen über viele Dateien hinweg, da deren Verarbeitung sehr zeitintensiv ist.
  • Nutzen Sie einen Datenextrakt anstatt einer Live-Verbindung, falls Sie nicht mit einem schnellen Datenbanksystem arbeiten (siehe Wann sollten Sie Datenextrakte und wann Live-Verbindungen verwenden).
  • Stellen Sie sicher, dass Sie beim Erstellen des Extrakts die Option »Einzelne Tabelle« wählen, anstatt der Option »Mehrere Tabellen« (siehe Abbildung 2.21). Dadurch wird das erzeugte Extrakt zwar größer und das Erstellen des Extrakts dauert länger, das Abfragen hingegen wird um ein Vielfaches beschleunigt.

Ausgewählte Option „Einzelne Tabelle“ im „Daten extrahieren“-Dialog
Abbildung 2.21: Ausgewählte Option »Einzelne Tabelle« im »Daten extrahieren«-Dialog

Empfehlungen für Performance-Optimierung bei Datenbank-Servern

Arbeiten Sie mit Daten auf einem Datenbank-Server, wie Oracle, PostgreSQL oder Microsoft SQL Server, und möchten die Zugriffszeiten verbessern? Dann achten Sie bzw. der dafür zuständige Datenbankadministrator zusätzlich auf folgende Punkte:

  • Definieren Sie für Ihre Datenbank-Tabellen sinnvolle Index-Spalten.
  • Legen Sie für Ihre Datenbank-Tabellen Partitionen an.

Dieser Beitrag ist der dritte Teil der Data-Operations-Serie:

Teil 1: Daten für die Analyse optimal vorbereiten
Teil 2: Wann sollten Sie Datenextrakte und wann Live-Verbindungen verwenden
Teil 3: Wie Sie die Performance Ihrer Datenanalyse und Dashboards steigern

Außerdem basiert dieser Blog-Post auf einem Unterkapitel des Buches „Datenvisualisierung mit Tableau„:

Datenvisualisierung mit Tableau
View on Amazon

How to Research LinkedIn Profiles in Tableau with Python and Azure Cognitive Services in Tableau

Azure Cognitive Services in Tableau: using Python to access the Web Services API provided by Microsoft Azure Cognitive Services
Azure Cognitive Services in Tableau: using Python to access the Web Services API provided by Microsoft Azure Cognitive Services

A few weeks after the fantastic Tableau Conference in New Orleans, I received an email from a data scientist who attended my TC18 social media session, and who is using Azure+Tableau. She had quite an interesting question:

How can a Tableau dashboard that displays contacts (name & company) automatically look up LinkedIn profile URLs?

Of course, researching LinkedIn profiles for a huge list of people is a very repetitive task. So let’s find a solution to improve this workflow…

Step by Step: Integrating Azure Cognitive Services in Tableau

1. Python and TabPy

We use Python to build API requests, communicate with Azure Cognitive Services and to verify the returned search results. In order to use Python within Tableau, we need to setup TabPy. If you haven’t done this yet: checkout my TabPy tutorial.

2. Microsoft Azure Cognitive Services

One of many APIs provided by Azure Cognitive Services is the Web Search API. We use this API to search for name + company + „linkedin“. The first three results are then validated by our Python script. One of the results should contain the corresponding LinkedIn profile.

3. Calculated Field in Tableau

Let’s wrap our Python script together and create a Calculated Field in Tableau:

SCRIPT_STR("
import http.client, urllib, base64, json
YOUR_API_KEY = 'xxx'
name = _arg1[0]
company = _arg2[0]
try:
headers = {'Ocp-Apim-Subscription-Key': YOUR_API_KEY }
params = urllib.urlencode({'q': name + ' ' + company + ' linkedin','count': '3'})
connection = http.client.HTTPSConnection('api.cognitive.microsoft.com')
connection.request('GET', '/bing/v7.0/search?%s' % params, '{body}', headers)
json_response = json.loads(connection.getresponse().read().decode('utf-8'))
connection.close()
for result in json_response['webPages']['value']:
if name.lower() in result['name'].lower():
if 'linkedin.com/in/' in result['displayUrl']:
return result['displayUrl']
break
except Exception as e:
return ''
return ''
", ATTR([Name]), ATTR([Company]))
view raw Tableau LinkedIn lookup with Python and Azure Cognitive Services.txt hosted with ❤ by GitHub

4. Tableau dashboard with URL action

Adding a URL action with our new Calculated Field will do the trick. Now you can click on the LinkedIn icon and a new browser tab (or the LinkedIn app if installed) opens.

LinkedIn demo on Tableau Public

Is this useful for you? Feel free to download the Tableau workbook – don’t forget to add your API key!

Get More Insights

This tutorial is just the tip of the iceberg. If you want to dive deeper into the world of data visualization and analytics, don’t forget to order your copy of my new book, Visual Analytics with Tableau (Amazon).  This comprehensive guide offers an in-depth exploration of data visualization techniques and best practices.

I’d love to hear your thoughts. Feel free to leave a comment, share this tweet, and follow me on Twitter and LinkedIn for more tips, tricks, and tutorials on Azure Cognitive Services in Tableau and other data analytics topics.

Also, feel free to comment and share my Azure Cognitive Services in Tableau tweet: