Creating Splunk Alerts (and Setting Permissions!) Through REST API

By: Marvin Martinez | Senior Developer

 

Creating Alerts via the Spunk REST API is fairly straightforward once you know exactly what parameters to use to ensure that Splunk recognizes the Saved Search as an Alert.  The same applies for ACL permissions on these alerts and other Splunk Knowledge Objects.

First things first, let’s create a scheduled search via REST using the “/services/saved/searches” endpoint.  The curl code below creates a simple search to pull some data from the _internal index for the last 10 minutes.

Note that, to create the saved search, all that was needed was authorization (a token in this case) and a couple of parameters in the call: (1) a name for the search and (2) the search itself.

This will create a search in the Searches, Reports and Alerts screen in Splunk Web.

As you can see, the search has been created and shows up as a Report.  But what if you need this to be an alert?! Even more importantly, what if you want to set this up with specific permissions?  Well, luckily, like essentially everything else in Splunk, this can also be done via the REST API.

To create a new search as an alert, you’ll need to call the same endpoint as shown above with the parameters mentioned below. Otherwise, call the “/services/saved/searches/{name}” endpoint if you’re modifying a search that’s already created.  For the purposes of this write-up, I will call the endpoint to manage an already created search (“/services/saved/searches/{name}”).

In order for Splunk to recognize the search as an alert, and not a Report, the following parameters have to be set correctly and passed along in your POST REST call.  The table below outlines the parameter name and a brief description of what they mean.

Parameter Description
alert_type ‘number of events’ (if this is set to ‘always’, which is the default, Splunk thinks it’s just a report)
is_scheduled true (this is a Boolean setting that Splunk checks to make sure there’s a set schedule for the report, which is required for alerts)
cron_schedule */10 * * * * (a cron schedule that represents the schedule which the alert will run on)
alert_comparator ‘greater than’ (this is the operator used in the alert settings to determine when to send the alert – associated with the alert_threshold below)
alert_threshold 0 (this is the number to compare with the operator above. i.e. only alert when results > 0)

 

The curl command for the REST call is shown below.  Note the aforementioned parameters that are now being included.

But how does this search now look in the Searches screen?  As can be seen in the image below, once the REST command has been executed successfully, your Alert should now be reflected appropriately as an “Alert”.

For further confirmation of these settings, click the Edit link under Actions, and click Advanced Edit from the drop-down menu.  This will bring up a lengthy listing of all the settings for this search.  If using the REST API is not your style, this is where you can alternately set these settings from Splunk Web.

The listing looks something like this:

All that’s left now is to set your permissions as desired.  To do this, you’ll need to call a new endpoint.  You’ll use the previous endpoint you used to manage a specific saved search, but you’ll add a new section at the end for “acl” (i.e. ‘https://localhost:8089/services/saved/searches/ATestRESTSearch/acl’).  This acl extension/option is available for any endpoint but, in this use case, we’ll use it to manage the permissions for the alert we created above.

In the case of a saved search, you’ll need to include the following parameters in your REST call:

Parameter Description
sharing ‘app’ – this can also be ‘global’ or ‘user’, depending on what the scope of the access you want this search to have (This is required when updating the ACL properties of any object)
app ‘search’ – this is the name of the app that this search belongs to.  (For saved searches, this is required when updating ACL properties of these objects)
perms.read A comma-delimited list indicating what roles to assign read permissions to
perms.write A comma-delimited list indicating what roles to assign write permissions to

 

A curl command that was used in this case is shown below.  In this example, the alert is being updated to give read permissions to admin and user-mmtestuser1.  Additionally, it is being updated to give write permissions to admin and power roles.

As an added bonus, here is an example of how Postman was leveraged to make this final call, in case that’s your REST API-calling tool of your choice.  The Authorization tab, in this example, was set to Basic Auth type with admin credentials.  In the Body tab, you’ll set your parameters to the REST call as “x-www-form-urlencoded” values.  Note the 4 parameters mentioned above shown included in the call below.

Once the REST call is made, navigate to your “Searches, Reports, and Alerts” screen in Splunk Web, and click to Edit Permissions of your alert.  You’ll notice that your permissions are now reflected just the way you designated them in your REST call.

The Splunk REST API is a great alternative, and a necessity for many, to using Splunk Web to create and manage knowledge objects.  Anything that can be done in Splunk Web can be done via the REST API, though it sometimes can be a bit hard to easily understand the process for how to achieve some of these desired actions.  Now, you can easily create alerts and set the permissions just the way you want…and all through REST!

Want to learn more about creating alerts via the Spunk REST API? Contact us today!

 

Using an External Application to Pull Splunk Search Results

By: Aaron Dobrzeniecki | Splunk Consultant

 

Have you ever wanted to pull logs from Splunk without actually being physically signed into the Splunk Search Head? With an external application, such as Postman, you can query the Splunk REST API endpoint to actually provide you with results from a search being run.

When Splunk runs a search, it creates a search ID which we can use to grab the results from the REST endpoint. We will be testing out two ways to get the results of a search. The first way is to grab the name of the Splunk search and query it against the /services/saved/searches/{search_name}/dispatch endpoint, which will provide us with the sid. We then use the sid to grab the results of the search, which will fire off the search and will poll for results as they come in. The second way to get the search results is by doing an export on the search name which will run the search and get the results without polling.

First things first, you need to make sure that the user you are authenticating to Splunk with has the “Search” capability, as well as access to search the necessary indexes. It’s that simple! If you are setting up a user for a particular person make sure they only have access to what they need. Giving further access is not necessary and can cause security issues.
In this example we are using the Postman application to query the Splunk REST API to grab search results from a couple of different reports/saved searches. Things we are going to need include:

  • Splunk user account with the Search capability. We need that user to be able to search the index we are going to be grabbing our data from.
  • We also need to know the Splunk URL we are going to be pulling from. In this case, I am using my localhost as an example. We will also be querying the Splunk management port of 8089 to get our results set.

The image above shows the type of request I am doing (POST), the REST API being used to query my search ID (/services/saved/searches/{name_of_search}/dispatch), and the authentication type of username and password. What the URL above is doing is it is reaching out to Splunk and grabbing the SID (search id) of the search named Index Retention Getting Close. With this search id we will be able to run a GET on the Splunk REST API and grab the results of the search.

Below I will be showing you two Splunk REST API endpoints that you can query (using POST) to get the Search ID for a specified search. The first endpoint is for searches that do not have Global permissions. As long as the user you are authenticating with has a role that has access to read the search, you can query the endpoint of /servicesNS/nobody/{app}/saved/searches/{name}/dispatch to retrive the Search ID. The second endpoint you can query if the search has Global permissions and you have read access is simply /services/saved/searches/{name}/dispatch to retrieve the Search ID. The two scenarios are below.

The image above shows the rest endpoint that can be used to grab a specific search ID that is in an app and has specific permissions. As long as my account has access to the app and search inside the app, I will be able to query it. For this example, we have changed the permissions of the search to be App only.

The image above is the results of the search in json, using the search ID we queried from the REST API.

The image above gives us the same results except they are in xml format.

The image above shows the search ID of the search with REST API I am querying. Since that search now has Global permissions, we do not need to use the ServicesNS endpoint. When you do a POST with a dispatch on the name of a search/report you will get the Search ID. As you can see the search ID is circled. We will be using this search ID to query the results of the search and show the actual search results in the Postman application. The Splunk REST API you will want to query next is the /services/search/jobs/{sid}/results?output_mode= (atom | csv | json | json_cols | json_rows | raw | xml). Any of those values will get you the results of the search in the format selected. In this example, I will be showing you json and xml.

As you can see above, the data results are shown in xml format for the search we were wanting to get results from.

This image shows the same results but in json format. With the options above for data output, you can query the Splunk REST API to get the search results and have them show in your preferred format.

Way 2: Query the REST API to show the results by using an export on the search name which will run the search and get the results without polling. Take a look at the screenshot below which queries the /services/search/jobs endpoint to stream in the results of the search as they come in.

Remember, you need to have the Search capability in Splunk, as well as you have to be able to read the results of the search. Whether that is setting Global permissions or having a role that has read access to the app and search. Below are some links referencing the Splunk REST API. If you have any questions at all regarding querying the Splunk REST API from an external application, please let me know!

https://docs.splunk.com/Documentation/Splunk/8.0.6/RESTTUT/RESTsearches

https://docs.splunk.com/Documentation/Splunk/8.0.6/RESTREF/RESTsearch#search.2Fjobs.2Fexport)

 

Want to learn more about using an external application to pull Splunk search results? Contact us today!

Driving Growth by Leveraging AWS and Document Understanding

Your company is sitting on a potential gold mine of stored data. Tucked away on servers and cloud-based drives are the answers and insights you need to take your business to that next level of growth. Advancements in machine learning and artificial intelligence have made it easier (and less expensive) to analyze this data through Document Understanding. Companies that leverage the Amazon Web Services (AWS) platform to support their needs, tying in a Document Understanding initiative can have a fundamental impact on driving growth and securing a more profitable bottom-line.

What is Document Understanding?

Historically, the chief hurdle to analyzing this data is that much of this data is unstructured – composed of text-based files, reports, survey results, social media posts, notes, and random PDFs. Sifting through this quagmire was expensive and inefficient as it had to be done by hand.

That was the old way.

Fueled by natural language processing (NLP) and machine learning (ML), these systems analyze text-based documentation (PDFs, notes, reports) to uncover insights. The machine-learning capabilities allow you to “teach” the AI how to read your specific documentation and guide its insight discovery.

How Document Understanding Can Benefit Your Enterprise Corporation

Enterprise companies are already tapping the power of AWS’s Document Understanding solution to garner essential insights into critical business functions.  Regardless of industry vertical, businesses are using Document Understanding to:

  • – Instantly search for information across multiple scanned documents, PDFs, images, reports, and stored text files.
  • – Redact critical information from documents and identify compliance threats in real-time.
  • – Digitize, store, and analyze customer feedback and request forms.
  • – Identify overarching communication trends and isolate specific messaging that can be used to improve the customer experience or marketing campaign.

And this is just the proverbial tip of the benefits iceberg. Through the machine learning aspect of Document Understanding, you can tailor your use of this technology to identify and analyze the data sets that have the most impact on your business and bottom line.

Driving Document Understanding through Intelligence with AWS Content Process Automation

As a certified AWS Advanced Consulting Partner, we are excited to announce the launch of our new AWS Content Process Automation (CPA) offering. Our new CPA tool integrates with the AWS platform to provide a structured process and streamlined toolset for implementing and managing an ongoing Document Understanding initiative.

Through our new AWS CPA offering, brands can:

  • – Make previously inaccessible data actionable at scale.
  • – Automate tendencies but necessary business processes.
  • – Improve compliance and risk management.
  • – Identify opportunities to increase operational efficiency and reduced costs.

How TekStream CPA Works

Historically, analyzing sizeable unstructured data sets for actionable information has been a time-consuming and costly initiative. Most of the work had to be done manually – which can be both costly and inefficient.

Our new CPA offering leverages artificial intelligence and machine learning, along with defined scope and direction, to increase the speed and accuracy for data discovery while eliminating much of the manual aspect of data mining.

Using machine-learning services like Amazon Textract and Amazon Rekognition, TekStream CPA inspects documents, images, and video (collectively called “files”), gathers key information and insights, and automatically stores these files logically to ensure easier access to critical information. Amazon Augmented AI (Amazon A2I) routes files requiring further review to content specialists and information managers to edit associated information, take corrective actions, and approve files for storage.

TekStream CPA relentlessly and automatically investigates content to find key insights and associations that might not be easily discovered by the naked eye. Users and administrators establish business rules defining what information is important, how it will be managed, and the storage rules for documents, images, forms, video files, and unstructured data. This ensures critical business facts and figures are available for business operations.

Built for Growth

Use these systems to gain a deeper understanding of internal and consumer audience sentiment around your brand or a specific product.

Analyzing your unstructured data sets is only part of the business growth equation. To achieve a true return on your investment and drive a noticeable impact on your bottom-line, you also need to transform your insights into actions. By leveraging serverless technologies like Amazon Lambda through our Content Process Automation tool, administrators can create functions to call their own services for file conversions, reformatting, and many more to meet specific business criteria.

Start driving business growth today. TekStream has deep experience helping clients across multiple industries accelerate their digital transformation and begin leveraging the power of Document Understanding to push their business forward. Reach out to us today to learn more about what CPA and Document Understanding can do for your business.

Want to learn more about unlocking value from your unstructured data? Download our latest eBook, “9 Steps to Unlocking Value from Your Unstructured Data and Content.”

8 Benefits to Using Document Understanding to Mine Unstructured Data

What if we told you that your business was sitting on a mountain of untapped business intelligence or that hidden away in archived emails, documents, and customer survey results are the very insights you need to drive growth and improve your bottom line? These types of text-based documents are a form of “unstructured data” and (alongside image libraries, data streams, and similar data deposits) account for nearly 80% of all the data that an enterprise company generates and stores.

How do you analyze all of this data to identify the specific insights that can drive change and improve performance in your organization? Through Document Understanding.

Understanding Document Understanding

Document Understanding is one of the three core AI capabilities fueling the unstructured data analysis industry (the other two being Computer Vision and IoT analysis). This system leverages the power of natural language processing and machine learning to analyze text-based documents (PDFS, notes, reports) to uncover actionable business insights.

The machine-learning capabilities of these systems allow your organization to “teach” the AI to read your specific documentation and discover insights that specific to your brand and audience.

8 Benefits of Analyzing Your Company’s Unstructured Data

The fact that the market size for natural language processing is estimated to reach over $16B by 2021 proves that organizations large and small are investing in tools and systems that analyze their unstructured data. This means that these companies are confident that the benefit of this work will outweigh the costs of these new systems.

While these benefits differ between industries, some of the key benefits to mining unstructured data includes:

1. Finding Opportunities to Improve Your Customer Experience

Retain more customers (and win over new fans) by using Document Understanding to analyze customer surveys and reviews to identify where your company can provide better customer service.

2. Discover New Opportunities in The Market

What is the “next big thing” in your industry? How will you ensure your company will stay relevant to consumers over the next 20 years? Turn your data lake into a blue ocean by mining your unstructured data for relevant insights and consumer trends.

3.  Know Your Audience Better With Sentiment Analysis

Use these systems to gain a deeper understanding of internal and consumer audience sentiment around your brand or a specific product.

4. Make Key Decisions Faster and More Accurately

Quit getting bogged down with analysis paralysis. Get the data you need to identify and take action on the “right” decision when it counts most.

5. Improve Team Productivity and Reduce/Remove Outdated Data Processing Techniques

Through automation, you can eliminate data processing bottlenecks and instead focus your employees on more high-value tasks.

6. Identify and Eliminate Unnecessary Cost Centers

Get a handle on your waste by understanding what areas of your business are costing you money (without providing a correlating ROI).

7.  Gain a Better Understanding of Your Customer Behavior and Buying Triggers

Improve the performance of your marketing campaigns and customer retention efforts by gaining more in-depth insight into what makes your customers your customers in the first place.

8.  Avoid Costly Regulatory or Compliance Issues

Uncover regulatory or compliance issues before they negatively impact your company.

Start with The End in Mind

Ready to get started analyzing your unstructured data, but not sure where to begin? We recommend starting with the end goal in mind. What is your highest unstructured data analysis priority? Are you sitting on a mountain of customer surveys? Are you curious about where your hidden costs centers are?

Understand which aspects of your unstructured data analysis will have an immediate impact on your business’s bottom line. Then work backward to develop the tools and systems you need to discover this intelligence.

If you are not sure where to begin, we can help. We’ve helped companies across a myriad of industries turn their unstructured data into business growth rocket fuel. Contact us today to learn how we can do the same for you.

If you’d like to learn more about how to unlock value from your unstructured data? Download our free eBook, “9 Steps to Unlocking Value from Your Unstructured Data and Content.”

Leveraging Machine Learning for Document Understanding

By: Troy Allen | VP Cloud Services

Businesses thrive on information, but due to the complexity and wide variety of data available within an organization, finding usable information can be challenging and time-consuming.  As organizations are inundated with documents, forms, data streams, and more, it’s becoming increasingly difficult to extract meaningful information efficiently, funnel that information into the systems that need it, and present it in a fashion that drives better business decisions.  Machine Learning (ML) and Artificial Intelligence (AI) tools are helping solve those challenges. ML and AI tools have rapidly become more sophisticated and capable of allowing organizations to gather critical information out of their content, rich media files, and data to facilitate better Document Understanding (interpreting unstructured documents into recognizable sets of information). Document Understanding is primarily focused on the information companies commonly mine: textual-based information, videos, and graphics. Amazon has recognized the importance of Document Understanding and developed services to help drive visibility and analysis that companies desperately need. Amazon’s Textract and Rekognition machine learning services are designed to gather meaning out of documents, rich media files, and data.

Getting More Out of Text

While Optical Character Recognition (OCR) has been around for many years, most organizations tend to overlook its strengths and ability to improve data processing.  Amazon Textract, while it does provide OCR functionality as a Cloud-based service, offers much more than one might expect because of its ability to bring Machine Learning-based models to business applications.  In order for data to be useful, it must first be collected; Textract goes beyond simple OCR by providing the ability to distinguish key-value pairs of information, table data extraction, and recognition of checkboxes and radio buttons.  Amazon Textract makes it easy to export the extracted data into a database or into off-the-shelf or custom applications. Traditional OCR solutions require additional tools to provide this level of data recognition and extraction.

More than OCR

Textract by Amazon Web Services goes beyond OCR by not only collecting the content but understanding where the content came from. Textract provides the ability to not only perform standard character recognition but is designed to understand formatting and how content is aligned within a page.  This is accomplished by recognizing and creating Bounding Boxes around key information and text areas to support the content, table extraction, and form extraction.

Amazon’s Textract retrieves multiple blocks of information from each page of the image it investigates:

– The lines and words of detected text
– The relationships between the lines and words of detected text
– The page that the detected text appears on
– The location of the lines and words of text on the document page

Table Data Exposed

Amazon’s Textract is well equipped to locate table data within documents. Textract recognizes the table construct and can establish key-value pairs with the cells by referencing the row and column information.

In addition to detecting text, Textract has the ability to recognize selection elements such as checkboxes and radio buttons.  A check box that has not been selected, such as  or Ο is represented as a status of NOT_SELECTED whereas checked boxes and circles are represented as SELECTED and can be tied to a key-value pair as well.  This can be extremely helpful in finding values in both tables and forms.

The Power of Key-Value Pairs

Businesses have been interacting with their clients and vendors for decades through forms. Textract provides the ability to read form data and clearly define key-value pairs of information from them.  Many organizations struggle with the fact that forms change over time, and it can be difficult to train legacy OCR tools to find data when those tools are specific to a particular form layout. Textract removes that limitation by reading the actual text rather than a location on a form to get its information and analyzes documents and forms for relationships between detected text.

Getting More from Images

Amazon Rekognition makes it easy to analyze image and video files using proven, highly scalable, deep learning technology that requires no machine learning expertise to use. Amazon Rekognition provides the ability to identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. It provides those capabilities while also delivering highly accurate facial analysis and facial search capabilities that can be used to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases.

Using AI to See More

With Amazon Rekognition Custom Labels, objects, and scenes in images can be identified for specific business requirements and actions. Models can be configured to classify specific machine parts, identify the use of Personal Protection Equipment (PPE) for employees from surveillance videos, capture model numbers in images, and detect persons of interest for image classification to name a few use cases. Amazon Rekognition Custom Labels allows organizations to quickly identify objects and images that have value to their specific business and processes.

Uncovering Hidden Data

As with Amazon Textract, Amazon Rekognition provides a way for companies to identify key information that can be stored, processed, and shared with other applications enabling greater Document Understanding across files and data.  Context of information is critical to assigning value and defining how it can be best utilized.

Amazon Rekognition helps companies realize value in their images and videos across many different use cases across the enterprise:

    • – Discover inappropriate content – filter images and videos for objects and scenes containing inappropriate content such as nudity, weapons, graphic violence, and even inappropriate text in the videos or images.
    • – Identify key objects – Amazon Rekognition can be utilized to filter Social Media video and image files to identify products, brands, people, and even landmarks.
    • – Help improve workplace safety – with the support of video, Amazon Rekognition can be utilized to inspect surveillance videos and identify issues such as people not wearing Personal Protective Equipment (PPE) and obstructive objects in the workplace.
    • – Support identity verification – facial recognition and person recognition can be accomplished through Amazon Rekognition by detecting humans, identifying facial features, and even comparing those to documented photographs of people for identifying people in images and video files.
    • – Capture text information – Amazon Rekognition also provides the ability to perform text capture and recognition in video and image files. This can help an organization gather data and information contextual to a video or image such as the model number of a part from a photograph of a manufacturer’s plate or even identify names of streets from street signs in a video to assist in determining the location of the event filmed.

You Are Not Alone

Business solutions can be complex, but making them work for your requirements doesn’t have to be.  Clearly defining your goals and objectives is half of the battle, the other half is knowing what tools will help you achieve those goals.  Are your off-the-shelf solutions and applications collecting all the information you have?  Do you need a business solution to manage all of your documents and data, but don’t know where to start?  Are you looking to move off an outdated legacy application that no longer supports your business direction?  You are not alone.

Thousands of companies are facing the same questions and are finding the best answers by engaging with experts from Amazon and experts from solution service providers. TekStream, along with Amazon, is excited to speak with you about your Document Understanding needs and how the right tools and solutions can have a positive impact on how you conduct business. TekStream is offering a free Digital Transformation assessment where we will work with you to identify your document processing needs and provide process and technology recommendations to help you transform your business with ease.  Reach out to us at info@TekStream.com for more details or call 1-844-TEK-STRM.

The Current State of Unstructured Data Analysis and What It Means for Your Business

The data analytics industry is continually evolving. What seemed like science fiction only years ago has become a business fact as new data analyzing tools and techniques come to market. And while larger organizations (with deeper pockets) have been the driving force of much of this change, advancements in artificial intelligence and machine learning have democratized big data analysis. Nowhere is this more apparent than the evolution of the modern, unstructured data analysis landscape.

Accounting for nearly 80% of all data generated and stored by an organization and growing at a rate of 55%-65% each year, unstructured data is one of the largest untapped and continuous sources of business intelligence. We’ve put this blog post together as an introduction to unstructured data and some of the tools and techniques that companies are using to analyze and mime their unstructured data for actionable insights to improve their organization and bottom line.

Understanding the 3 Different Types of Big Data

To understand what is meant by the term “unstructured data,”  you first need to know where it falls within the broadest categories of business data – structured data, semi-structured data, and unstructured data.

The below table compares the overarching differences between these data sets.

Structured Data Semi-Structured Data Unstructured Data
  • ●      Historically used for data analysis and mining.

 

  • ●      Data that is loosely organized by its source and delivery channel:
    • ○      Email
    • ○      Tweets
    • ○      Folders
  • ●      Data that is not organized in any way, which makes it difficult to process and analyze using traditional methods.
    • ○      Surveys
    • ○      External Industry Reports
    • ○      Data Analysis
  • ●      Designed for data capture, data input, data analysis, search, etc. within the document.
  • ●      Has some basic search/discovery
    • ○      Inbox Search
    • ○      Hashtags
    • ○      Folder Names
  • ●      Tends to be text-heavy but can include voice recordings, images, video, etc.
    • ○      Notes (handwritten or typed)
    • ○      Documents (POs, Resumes, Invoices)
    • ○      Rich Media (Geo-Spatial, Security, etc.)
    • ○      Analytics/Performance Data
    • ○      Internet of Things Usage Reports/Data Streams
    • ○      Customer Communications (Surveys, Live Chat, Automated Messaging)
  • ●      Pre-defined structured format with standardized columns and rows:
    • ○      Databases, Google Sheets, CSV, Excel, etc.
  • ●      Specific data within these channels is unstructured and text-heavy.

 

  • ●      Also known as qualitative data.
  • ●      Tends to be utilized within an organization according to data type.

As you can see, the definition of unstructured data is broad. There is no consistent medium or format, but most unstructured data is unstructured text: documents, social media posts, emails, surveys, etc.

So, how does a large organization mine swathes of unstructured data for nuggets of actionable gold?

The Current Environment and Capabilities for Mining Unstructured Data

As the importance of mining unstructured data grows, new discovery and intelligence tools are introduced to the market. Simultaneously, as analysis technology improves, more companies build systems and tools that have integrated data logging capabilities capturing and generating more unstructured data than in previous years.

This means that not only do companies of all sizes have access to more advanced data mining tools – they also have more extensive data sets to analyze.

In general, there are three core AI capabilities that are empowering unstructured data analysis:

Document Understanding: Fueled by natural language processing (NLP) and machine learning (ML), these systems analyze text-based documentation (PDFs, notes, reports) to uncover insights. The machine-learning capabilities allow you to “teach” the AI how to read your specific documentation and guide its insight discovery.

Computer Vision: This is used to analyze image and video content through digital imaging technologies, pattern recognition, and ML in order to process your visual data and uncover actionable intelligence.

Internet of Things: Here, data is generated from machines. AI relies on real-time analytics, ML, and smart systems to analyze the data for performance-improvement insight.

Document Understanding and Text-Based Data Analysis

We stated this earlier, but the vast majority of an organization’s unstructured data is text-based. More organizations are leveraging the power of Document Understanding systems to drive data mining and identify impactful findings.

Here are just a few examples of how companies are leveraging document understanding to fuel insight gathering:

  • – Sentiment Analysis: Automatically classify text by sentiment and pull together trend reports.
  • – Keyword Extraction: What keywords are recurring throughout a data set
  • – Regulatory and Compliance Support: Identify regulatory or compliance issues before they impact your business.

Getting Started with Unstructured Data Analysis

Ready to get started mining your unstructured data? TekStream has deep experience deploying both pre-built and custom unstructured data analysis solutions that empower teams with the insights they need to take action and improve their bottom line. Contact TekStream today to learn more about how we can assist your company with its unstructured data analysis goals.

Are you looking for more insights and best practices for unlocking value from your unstructured data? Download our free eBook, “9 Steps to Unlocking Value from Your Unstructured Data and Content.”

TekStream Launches AWS Content Process Automation Solution

TekStream, an Atlanta-based digital transformation technology firm, is excited to announce the launch of their new Content Process Automation (CPA) Solution. This new CPA solution enables users to quickly process and manage critical business documents, images, forms, video files, and unstructured data from a wide variety of sources through the power of AWS Integrated Services.

TekStream’s CPA solution automatically investigates content to find key insights and associations that might not be easily discovered by the naked eye. Users and administrators establish business rules defining what information is important, how it will be managed, and the final destination of documents, images, forms, video files, and unstructured data to ensure critical business facts and figures are available for business operations. Each department within an organization can benefit from TekStream’s CPA solution, but it may be of most use to departments including:

– Legal
– Human Resources
– Information Technology
– Marketing
– Sales

“TekStream has always focused on understanding our clients’ needs and providing solutions that ensure their success in reaching and exceeding expectations. Our new Content Process Automation offering is based on years of helping customers gain better insights from their managed content. With the power of AWS Artificial Intelligence and Machine Learning, we were able to create a solution that makes obtaining operational success that much easier to reach. We also ensured that our design is future-proofed by allowing more AWS services to be incorporated based on client needs or as new and more powerful services are offered by AWS,” said Troy Allen, Vice President of Cloud Services at TekStream.

Content Process Automation is a platform to build upon, as your business needs grow, TekStream’s CPA capabilities will grow to meet your requirements. As Amazon continues its investment in machine learning and artificial intelligence, TekStream’s CPA solution will incorporate those services to provide more options to make document understanding easier and more efficient.

TekStream

TekStream accelerates clients’ digital transformation by navigating complex technology environments with a combination of technical expertise and staffing solutions. We guide clients’ decisions, quickly implement the right technologies with the right people, and keep them running for sustainable growth. Our battle-tested processes and methodology help companies with legacy systems get to the cloud faster, so they can be agile, reduce costs, and improve operational efficiencies. And with 100s of deployments under our belt, we can guarantee on-time and on-budget project delivery. That’s why 97% of clients are repeat customers. For more information visit https://www.tekstream.com/.