SharePoint and Machine Learning – Harnessing the Potential of Corporate Big Data with Intelligence and Automation

Five years back, SharePoint was limited to being a small to mid-size data tool. Having posed some limitations for big data SharePoint would not be an ideal candidate for machine learning algorithms – as SharePoint had limitations such as the number of items/files in SharePoint lists 2000-5000).

However, the tides have been shifting over the last few years, with big data taking a front row seat in IT development, and the larger dataset support in SharePoint today.

Currently, structured data holds more potential and value to corporations, thanks to its ability to power machine learning algorithms. How is SharePoint accommodating big data so as to avoid becoming a contender in powering machine learning algorithms?

We will consider practical ways in which organizations can harness the potential of machine learning in their use of SharePoint. But before that, let us take a closer look at the role of machine learning in the corporate setting.

Machine Learning in the Corporate Environment

There are five main areas of machine learning application in the corporate setting, and these areas represent the basic machine learning algorithms.

  1. Classification – In the business setting, you may have data points or even tasks that need classification into separate categories. Classification is a supervised learning approach that takes data input, learns from it and labels it accordingly.
  2. Anomaly detection – The identification of outliers, which are data points that stand out from the rest is yet another role of machine learning. It comes in handy when evaluating performance or results, particularly so when there is overwhelming data from which to extract conclusions.
  3. Regression analysis – Using this aspect of machine learning, businesses can use one or several predictor variables to predict a continuous outcome. It offers a numerical value that could help in revenue and cost predictions as well as sales prognosis.
  4. Clustering – Just like classification, clustering involves grouping data points. But the key difference is that the latter uses an unsupervised approach for the analysis. In the corporate setting, it often creates a base for recommendation engines based on client target groups.
  5. Reinforcement learning – These are algorithms that learn from past behavior and are ideal for tasks that require minimal human intervention. They can be supervised or unsupervised and are handy for companies that use automation extensively.

The Best of Both Worlds – Machine Learning in SharePoint

SharePoint has been a hallmark of collaborative projects and to remain relevant, it has kept pace with changes in technology. In relation to machine learning, the program is now fully capable of working with big data for remarkable results.

Take a look at some of the areas in which a marriage between the two is proving indispensable to businesses large and small.

  • Using Flow for Cognitive Analysis

The program’s cognitive service engine has the capacity to take any data input and carry out an extensive analysis. This could be in areas such as sentiment analysis, moderation, identification of key phrases as well as translation.

A major highlight of this feature is the fact that all functionality is directly linked to Microsoft Flow. That means that anyone using the program, not necessarily an IT guru, can get a sentiment score for any given piece of data and from there, take the required action. This can be really handy for posts such as tweets.

  • Row Formatting

Among the recent features in SharePoint is a new intelligent row formatter. It works in more or less the same way as the column formatter that was introduced in 2017. With this feature, you can define conditional formats using scripts, and apply them to an entire row.

Moreover, the formats can take over a full screen, showcasing a responsive design which essentially makes it the complete app solution.

  • Image Analytics

Anytime you upload an image on Office 365, the system automatically scans it for object recognition. Thanks to this provision, it can identify         business cards, receipts, people as well as whiteboards among other things.

Furthermore, thanks to intelligent automation the program can identify extractable text images and geolocation data. It then tags these in metadata columns on the various document libraries (SharePoint Online List column name ‘OCR_ExtractedText) so as to facilitate easy filtering and searching.

  • Custom Image Algorithms

Basic image recognition can go a long way in simplifying processes. However, when you have to sort thousands of custom images for custom objects, you may require something more. And that is where machine learning comes in.

When dealing with images such as designs or logos, you can use cloud cognitive services along with SharePoint to make the process intelligent. Using the service, you can build, test and also deploy custom machine learning models. With these, it will be possible to add visual intelligence to content or processes.

In case you do not fancy creating a custom model, it is also possible to use Flow on Office 365 to invoke existing models.

  • Creating New Lists

Building lists on SharePoint is now so much easier thanks to machine learning and this makes it a great platform for the centralization of business data. You can easily copy lists from elsewhere, import them from Excel or even reuse existing templates for data commonly used in your organization.

Curating the list to suit custom needs is also easy thanks to the handy features in the program. You can select the type of list to create and even add desired properties from a long list of templates.

SharePoint lists are designed to store structured data using ‘metadata’. Having a good percentage of content that has proper metadata, is a good candidate to train other documents to be automatically tagged, classified, analyzed for reports, and actioned against.

  • Editing in Place

When you have to move your data from another source such as Excel, it is now easy to retain ease of use. Editing in place simply means the ability to click on a value and modify it without having to switch back and forth from “Edit Mode” or using the Properties page.

This helps provide the ‘metadata’ to keep your content and information properly ‘described’. In the long run, this will power search, analytics, reporting and future machine learning algorithms to help make predictions and auto-populate or tag new files.

  • Real-Time Updates and Office 365 Integrations

Machine learning has made it easier for SharePoint users to link list items to other apps on Office 365. Such apps include Outlook, Planner, Locations and others. Additionally, real-time updates make it possible to view libraries and lists without the constant need to refresh pages manually.

  • Integration with Power BI

SharePoint Lists contain plenty of critical data, updated in real-time. Optimizing such data requires that you find a way to access insights and visualizations against it. Thanks to Power BI integration, you can automatically mine reams of data for charts and patterns.

To make things even better, the intelligent system can pin the insights to your list’s homepage so that the entire team can gain easy access.

See how Power BI now offers you the ability to use Machine Learning to build powerful reports/insights:

  • Designing and Automating Workflows (Microsoft Flow + Visio)

Business users appreciate the value of Visio in capturing business processes thanks to its rich capabilities in modeling. With the marriage of machine learning and SharePoint, the functionality is further enhanced.

Users can now employ Visio to create Microsoft Flow workflows. Thanks to this capability, collaboration with multiple stakeholders is much easier using Visio’s commenting and sharing capabilities. From there you can publish the workflow using a single click to MS Flow and supply the necessary parameters for activation.

  • Text Analytics

Machine learning algorithms can classify the numerous documents on SharePoint and tag them appropriately. They can also implement suitable management systems to facilitate accurate and fast retrieval. The tool is highly functional in various areas of an organization like document classification and HR application evaluation.

Making the Most of Big Data

Considering the current value of data in the business world, the use of machine learning in various SharePoint applications is indispensable. It has enhanced the program’s capabilities and allows corporates to make the most of big data. The above practical ways in which machine learning has injected intelligence and automation into the platform highlight the limitless potential of the two and their practicality in business.

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Janica San Juan

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