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Use Case: White-Labeled Private Platform for Enterprise Data Collaborations
by
Nuklai

Summary:

Nuklai’s data consortium solution is designed to address the complexities of data sharing, tackling the concerns surrounding data privacy, security, and compatibility. This collaborative environment for businesses complies with international regulatory data standards and lightens the financial and technical loads associated with data privacy and integration. As a result, enterprises can maintain sovereignty over their essential data while capitalizing on the collective intelligence of their partners for operational and competitive edges.

Backed by insights from industry titans like Deloitte, McKinsey, and Gartner, this initiative has proven to not only slash data management expenses but also to potentially triple economic returns. This boosts the profitability of enterprises by enhancing market agility and informed strategic decisions.

Challenge: Overcoming Barriers to Data Sharing in Business: Privacy, Compliance, and Technical Hurdles

In today's increasingly interconnected and competitive business world, the importance of collaboration and shared intelligence on the data level cannot be overstated. When companies move beyond competitive barriers and concentrate on creating mutual value, new business opportunities emerge, and innovation flourishes.

Enterprises frequently possess vast amounts of data; yet find themselves in a compartmentalised landscape with restricted agility to adapt quickly to market changes. Sharing data with trusted partners is an increasingly viable strategy for businesses to enhance mutual value. By intensifying cooperation with partners within the same value chain or across domains, they can achieve operational advantages and carve out a competitive edge.

Despite the numerous advantages of sharing data with trusted partners, suppliers, and other businesses, there's often a reluctance to initiate this process. Central to this hesitation are concerns about data privacy and security. Regulatory compliance further complicates matters, as data protection regulations differ widely across countries and industries. The repercussions of non-compliance can be significant, including substantial fines and damage to reputation. Navigating this complex regulatory landscape increases legal expenses for enterprises.

Moreover, it's completely reasonable for enterprises to guard their most vital data, which is at the core of their strategic insights and competitive edge. The notion of sharing data is often viewed by businesses as a potential threat to their unique market position. There's a common misconception that sharing data might water down their proprietary insights, equalizing the competitive landscape and nullifying their hard-earned advantages. This concern is especially pronounced in industries characterized by rapid innovation cycles and short windows of opportunity.

Another significant challenge is the technical difficulty involved in data integration of internal and external data sources. Combining data from various sources, each with its unique format, structure, and level of detail, poses a formidable challenge for any engineering team. The necessary investment to standardize and harmonize the data, as well as the continuous effort to maintain this uniformity, demands considerable financial and operational resources. This also hinders enterprises from making the data available to others within their data consortiums. Consequently, the technical and financial demands of this undertaking are viewed as substantial barriers to entry, particularly for smaller companies with limited resources.

These three primary challenges significantly contribute to the cautious stance companies take toward data sharing. Although the benefits are evident, the perceived risks and obstacles often loom larger for companies contemplating this collaborative move. In the absence of definitive solutions to these issues, many organizations choose to keep their data in silos, missing out on potential gains.

Objectives: Creating Collaborative Value through Data Consortiums: Balancing Privacy, Compliance, and Competitive Advantage

After engaging with numerous enterprises and understanding their challenges, we're committed to forming data consortiums.

Our primary objective with this data consortium technology is to form a secure space where businesses can share their data confidently, without compromising their privacy or losing their competitive edge. We are acutely aware of the complexities involved in regulatory compliance. Our private data consortium is designed to simplify the legalities of data sharing. We implement stringent security protocols and ensure compliance with international data protection standards, minimizing the risk of unauthorized access and data breaches, thereby cutting down on legal and compliance expenses.

By offering a platform where data is readily exchanged, our goal is to dismantle the barriers that hinder collaboration and quick market reaction. We're focused on lessening the technical and financial burdens that come with data integration. Through our proprietary data standardization engine, we streamline and unify data, eliminating the need for individual businesses to heavily invest in technology and its upkeep. This allows even smaller companies with fewer resources to tap into the power of shared data intelligence, cultivating a more inclusive business landscape where new value can be generated. Whether it’s integrating data stored on-premises or in the cloud, our connector technology is designed to link your data sources to your private network with ease.

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Contrary to concerns about losing their competitive edge, our private data networks actually enhance an enterprise's advantage by providing members with access to a wider array of data. This information can be honed for strategic decisions and operational improvements. The collective intelligence gathered is designed to yield a richer and more accurate insight into market trends, customer behaviors, and strategic decision-making. All the while, companies maintain full control over which data—or even specific parts of their data—they choose to share with their trusted or broader business partners. Enterprises have the power to regulate access to their data, opting not to share data with competitors but instead choosing to share select data with those in their value chain.

Our private data consortiums are not only to safeguard valuable data, but can also act as a way to expand the reach of data outside the consortium. Whether the data is maintained exclusively within the private network or a strategic decision is made to share the data publicly; our solution gives the flexibility to make these choices at any point in time. When dynamics in the market shift, enterprises have the flexibility to adjust their data strategy so it directly aligns with its business goals. Ultimately, data consortiums empower companies to navigate the complex data landscape with more ease.

Result: The Economic Upside of Data Consortiums: Cost Savings, Efficiency, and Enhanced Decision-Making

In a Deloitte survey about the advantages of consortiums, 57 percent of organizations identified cost savings as the top benefit, indicating that consortiums can significantly lower operational expenses and indirectly boost profitability. McKinsey has also reported that organizations can cut down on data-related costs by merging information from different sources and businesses, which helps to simplify data management processes. Maintaining scattered data repositories can take up to 20 percent of a firm's IT budget, so consolidating these repositories can result in considerable savings and increased operational efficiency.

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Image Source: McKinsey Digital: Reducing data costs without jeopardizing growth (july 2020)

In the near term, companies may see a reduction of 5 to 15 percent in their annual data management costs. Over the long haul, these savings could nearly double as businesses integrate cutting-edge technologies and re-engineer their core processes around data management. Strategic data handling can enable firms to recover and reallocate as much as 35 percent of their present annual data expenditure.

Gartner indicates that companies that partake in external data sharing realize considerably greater measurable economic benefits than those that don't. They report that data and analytics leaders who distribute their data beyond their own organizations can gain three times more economic benefit than those restricting data sharing to internal stakeholders. Forbes has also reported that leveraging third-party data sources boosts not just operational efficiency, but also reduces time to market. This is exemplified by an insurer's case, where they were able to integrate and adapt data from external sources into their existing processes 80 percent faster than using their own data.

By creating private data consortiums, we anticipate that enterprises will deepen their insights and refine their decision-making processes. The collective advantages result in significant cost reductions and heightened operational efficiency, culminating in improved profitability.

Conclusion: Leveraging Private Data Consortiums for Agility and Compliance

Our (private) data consortiums present a solution for businesses grappling with the need for agility in a dynamic, data-driven world and the imperative of adhering to stringent data regulations. These private networks not only facilitate the secure exchange of data but also instill confidence that the data shared is appropriate while minimizing the risks of privacy violations and regulatory non-compliance. By breaking down data silos and integrating data more effectively, the consortiums ensure that enterprises of any size can achieve operational and competitive advantages from data sharing.

Support from industry authorities such as Deloitte, McKinsey, and Gartner underlines the substantial value that such collaborative networks provide. They notably cut costs and reveal the possibility of tripling the economic benefits through external data sharing. As part of our ecosystem, businesses will obtain deeper market insights, optimize their decision-making capabilities, and accelerate their market response. This lays a robust groundwork for innovation and sustained profitability in the fast-paced digital economy.

Want to talk to our team of experts about leveraging Nuklai for your data collaborations? Get in touch here.

About Nuklai

Nuklai is a collaborative data marketplace and the infrastructure provider for data ecosystems. It brings together the power of community-driven data analysis with the datasets of some of the most successful modern businesses.

The marketplace allows both grassroots data enthusiasts and institutional partners to find new ways to put untapped data to use and find new revenue streams. Our vision is to unify the fragmented data landscape by providing a user-friendly, streamlined, and inclusive approach to sharing, requesting, and evaluating data for key insights, better processes, and new business opportunities, empowering next generation Large Language Models and AI.

Javascript
Python
PHP
const ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries";
const ApiKey = "[API_KEY]";
const DatasetId = "[DATASET_ID]";

const headers = {
  "Content-Type": "application/json",
  'authentication': ApiKey
}

ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries"
ApiKey = "[API_KEY]"
DatasetId = "[DATASET_ID]"

headers = {
  "Content-Type": "application/json",
  "authentication": ApiKey
}

$ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries";
$ApiKey = "[API_KEY]";
$DatasetId = "[DATASET_ID]";

$headers = [
  "Content-Type: application/json",
  "authentication: $ApiKey"
];

Sources:

Deloitte 2019 Global Blockchain Survey

McKinsey Reducing data costs without jeopardising growth

Gartner Data Sharing Is a Business Necessity to Accelerate Digital Business

Forbes Why Businesses Need Third-Party Data Marketplaces — And Fast
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Javascript
Python
PHP
// @dataset represents your dataset rows as a table
const body = {
  sqlQuery: "select * from @dataset limit 5",
}

@dataset represents your dataset rows as a table
body = {
  "sqlQuery": "select * from @dataset limit 5"
}

// @dataset represents your dataset rows as a table
$body = [
  "sqlQuery" => "select * from @dataset limit 5"
];
Javascript
Python
PHP
cURL
const ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries";
const ApiKey = "[API_KEY]";
const DatasetId = "[DATASET_ID]";

const headers = {
  "Content-Type": "application/json",
  'authentication': ApiKey
}

// @dataset represents your dataset rows as a table
const body = {
  sqlQuery: "select * from @dataset limit 5",
}

// make request
fetch(ApiUrl.replace(':datasetId', DatasetId), {
  method: "POST",
  headers: headers,
  body: JSON.stringify(body), // convert to json object
})
  .then((response) => response.json())
  .then((data) => {
    console.log(data);
  })
  .catch((error) => {
    console.error(error);
  });

import requests
import json

ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries"
ApiKey = "[API_KEY]"
DatasetId = "[DATASET_ID]"

headers = {
  "Content-Type": "application/json",
  "authentication": ApiKey
}

# @dataset represents your dataset rows as a table
body = {
  "sqlQuery": "select * from @dataset limit 5"
}

# make request
url = ApiUrl.replace(':datasetId', DatasetId)
try:
  response = requests.post(url, headers=headers, data=json.dumps(body))
  data = response.json()
  print(data)
except requests.RequestException as error:
  print(f"Error: {error}")

$ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries";
$ApiKey = "[API_KEY]";
$DatasetId = "[DATASET_ID]";

$headers = [
  "Content-Type: application/json",
  "authentication: $ApiKey"
];

// @dataset represents your dataset rows as a table
$body = [
  "sqlQuery" => "select * from @dataset limit 5"
];

// make request
$ch = curl_init(str_replace(':datasetId', $DatasetId, $ApiUrl));

curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($body)); // convert to json object
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, TRUE);

$result = curl_exec($ch);
curl_close($ch);

echo $result;

curl -X POST 'https://api.nukl.ai/api/public/v1/datasets/[DATASET_ID]/queries' \
  -H 'Content-Type: application/json' \
  -H 'authentication: [API_KEY]' \
  -d '{"sqlQuery":"select * from @dataset limit 5"}'
Javascript
Python
PHP
cURL
const ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries/:jobId";
const ApiKey = "[API_KEY]";
const DatasetId = "[DATASET_ID]";
const JobId = "[JOB_ID]"; // retrieved from /queries request

const headers = {
  "Content-Type": "application/json",
  'authentication': ApiKey
}

// make request
fetch(ApiUrl.replace(':datasetId', DatasetId).replace(':jobId', JobId), {
  method: "GET",
  headers: headers
})
  .then((response) => response.json())
  .then((data) => {
    console.log(data);
  })
  .catch((error) => {
    console.error(error);
  });

import requests

ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries/:jobId"
ApiKey = "[API_KEY]"
DatasetId = "[DATASET_ID]"
JobId = "[JOB_ID]"  # retrieved from /queries request

headers = {
  "Content-Type": "application/json",
  "authentication": ApiKey
}

# make request
url = ApiUrl.replace(':datasetId', DatasetId).replace(':jobId', JobId)
try:
  response = requests.get(url, headers=headers)
  data = response.json()
  print(data)
except requests.RequestException as error:
  print(f"Error: {error}")

$ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries/:jobId";
$ApiKey = "[API_KEY]";
$DatasetId = "[DATASET_ID]";
$JobId = "[JOB_ID]"; // retrieved from /queries request

$headers = [
  "Content-Type: application/json",
  "authentication: $ApiKey"
];

// @dataset represents your dataset rows as a table
$body = [
  "sqlQuery" => "select * from @dataset limit 5"
];

// make request
$ch = curl_init(str_replace(array(':datasetId', ':jobId'), array($DatasetId, $JobId), $ApiUrl));

curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, TRUE);

$result = curl_exec($ch);
curl_close($ch);

echo $result;

curl 'https://api.nukl.ai/api/public/v1/datasets/[DATASET_ID]/queries/[JOB_ID]' \
  -H 'Content-Type: application/json' \
  -H 'authentication: [API_KEY]'