Toronto, ON
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Data Acquisition and Enrichment

Data Ingestion

We help you acquire raw data from multiple data sources or generate synthetic data to train your machine learning models. We obtain raw data and convert it to a format that can be easily manipulated depending on your use case. The data ingestion layer is the backbone of any analytics architecture. Downstream reporting and analytics systems rely on consistent and accessible data.

Here are few challenges that our ingestion technology and stack help address, and we help our clients innovate and discover great potential through wise investment decisions:

Slow

Enterprise Data is scaling and growing at a much larger pace, more complex and diverse, and the old methods of data ingestion just aren’t fast enough to keep up with the volume and scope of modern data sources.
Complex

Because there is an explosion of new and rich data sources like smartphones, smart meters, sensors, and other connected devices, companies sometimes find it difficult to get the value from that data. This is, in large part, due to the complexity of cleansing data — such as detecting and removing errors and schema mismatches in data.
Expensive

The infrastructure needed to support the different data sources and proprietary tools can be very expensive to maintain over time and maintaining a staff of experts to support the ingestion pipeline is not cheap. Not only that, but real money is lost when business decisions can’t be made quickly.
Insecure

Security is always an issue when moving data. Data is often staged at various steps during ingestion, which makes it difficult to meet compliance standards throughout the process.
There are different ways of ingesting data, and the design of a data ingestion layer can be based on various models or architectures including but not limited to Batch & real-time streaming ingestion. The right ingestion model supports an optimal data strategy, and businesses typically choose the model that’s appropriate for each data source by considering the timeliness with which they’ll need analytical access to the data. Our solution incorporates all below great advantages from cutting edge integration technologies to address previous listed challenges:
Faster and flexible

When you need to make big decisions, it's important to have the data available when you need it. With an efficient data ingestion pipeline, we help cleanse your data or add timestamps during ingestion, with no downtime. And you can ingest data in real time, in batches, or using a lambda architecture.
Less complex

While you may have a variety of different sources with different data types and schemas, a well-designed data ingestion pipeline should help take the complexity out of bringing these sources together. Our experts have excelled in sourcing data from hundreds of data sources into your target data warehouse be it cloud or on premises. Intelsoft Services can help translate from an on-premises schema, such as Oracle, to whatever schema you're using in your data warehouse.

Cost efficient

Well-designed data ingestion should save your company money by automating some of the processes that are costly and time-consuming. In addition, data ingestion can be significantly cheaper if your company isn’t paying for the infrastructure to support it. At Intelsoft Services, we focus on helping you save money by reducing infrastructure costs, but you also save money when we help automate your data ingestion process — allowing you to make the business decisions that save your company money in a timely manner.
Secure

Moving data is always a security concern. But security is baked into the DNA of our experts and technology stack we choose based upon your existing platform and this is an area where we shine.




Data Enrichment

We help clients enhance the data they already possess so they can make more informed decisions and to make their raw data more useful. By adding data from third parties, we can help our clients gain deeper insight into their customers’ lives and enable them to launch more personalized campaigns. In many cases, this means combining internal data with information received from external sources. This is a popular way for brands to augment their existing data sets and improve the quality of the conclusions that they can draw.

Businesses carry out Data Enrichment to improve the information they currently have so they can make better-informed decisions. Apart from that, it helps businesses perform the following operations:

  • Define and manage hierarchies
  • Create new business rules for labeling and sorting data on the fly
  • Investigate and process data that is multilingual and multi-structured
  • Process text and Semi-structured Data more efficiently
  • Reduce costs and optimize sales
  • Perform Predictive Analysis

This enriched data provides the opportunity for a more granular analysis than would otherwise be possible. Whether it is a more in-depth picture of a business’s audience, or better understanding of their competitors’ behavior, the added power of enriched data can make it much more useful than its constituent sources are on their own.

1. Higher Data Quality

Data Enrichment is a continuous process. It must be performed on a regular basis as the demands of the consumers change. Experts at Intelsoft may help build a continuous enrichment process by scraping internet data sources and collecting data from them.
2. Robust ETL Pipeline

After the data quality check and data cleaning, it is now ready to be combined with the current database. This is performed through ETL (Extract, Transform, and Load) processes to guarantee that all production systems have the most recent data.
3. Data Matching and Data Deduping

Extrapolating Data is another process of Data Enrichment effort. Engineers can extract more information from a raw data source using fuzzy logic techniques. After data cleansing, it is important to match data with existing duplicates.
4. Data Segmentation

It is always better to group or catalog the data into specific tags. The more businesses can narrow and “tighten” data sets to focus on certain target segments, the more Data Enrichment operations will pay off.
5. Updating and Monitoring Data

Data should be regulated such that it is periodically cleaned and validated to ensure consistent and continuous data quality in the future. Monitoring data entails setting up controls that it is compliant with data quality and business requirements.

Data Cleansing

Our data specialists will help you improve your business processes by cleansing or scrubbing or appending and achieve goals of correcting or removing inaccurate and corrupt data. This process is crucial and emphasized because wrong data can drive a business to wrong decisions, conclusions, and poor analysis, especially if the huge quantities of big data are into the picture.

1. Remove duplicate or irrelevant observations

Our experts will help identify and remove unwanted observations from your dataset, including duplicate observations or irrelevant observations. Duplicate observations will happen most often during data collection.
2. Fix structural errors

Structural errors are when you measure or transfer data and notice strange naming conventions, typos, or incorrect capitalization. These inconsistencies can cause mislabeled categories or classes. For example, you may find “N/A” and “Not Applicable” both appear, but they should be analyzed as the same category.
3. Filter unwanted outliers

Often, there will be one-off observations where, at a glance, they do not appear to fit within the data you are analyzing. If you have a legitimate reason to remove an outlier, like improper data-entry, doing so will help the performance of the data you are working with.
4. Handle missing data

You can’t ignore missing data because many algorithms will not accept missing values. There are a couple of ways to deal with missing data. Neither is optimal, but both can be considered.
5. Validate and perform QA

False conclusions because of incorrect or “dirty” data can inform poor business strategy and decision-making. False conclusions can lead to an embarrassing moment in a reporting meeting when you realize your data doesn’t stand up to scrutiny.

When combining multiple data sources on data integration or warehousing platforms, there are many opportunities for data to be duplicated or mislabeled. Our experts tackle complex data challenges and excel at fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.

Having clean data will ultimately increase overall productivity and allow for the highest quality information in your decision-making. Benefits include:

  • Removal of errors when multiple sources of data are at play.
  • Fewer errors make for happier clients and less-frustrated employees.
  • Ability to map the different functions and what your data is intended to do.
  • Monitoring errors and better reporting to see where errors are coming from, making it easier to fix incorrect or corrupt data for future applications.
  • Using tools for data cleaning will make for more efficient business practices and quicker decision-making.

Data Integrity

We ensure that the integrity of data is secure, the information stored in a database will remain complete, accurate, and reliable no matter how long it’s stored or how often it’s accessed. Data integrity describes both the state of data (i.e., valid or invalid) and the process for achieving the valid state with tactics such as error checking and anomaly detection. We can handle all your security and IT needs, enabling your business to be more stable, successful and profitable. We consider your specific goals and match them with what’s the most important for you and your employees.

Entity integrity

Entity integrity relies on the creation of primary keys — the unique values that identify pieces of data — to ensure that data isn’t listed more than once and that no field in a table is null. It’s a feature of relational systems which store data in tables that can be linked and used in a variety of ways.
Referential integrity

Referential integrity refers to the series of processes that make sure data is stored and used uniformly. Rules embedded into the database’s structure about how foreign keys are used ensure that only appropriate changes, additions, or deletions of data occur. Rules may include constraints that eliminate the entry of duplicate data, guarantee that data entry is accurate, and/or disallow the entry of data that doesn’t apply.
Domain integrity

Domain integrity is the collection of processes that ensure the accuracy of each piece of data in a domain. In this context, a domain is a set of acceptable values that a column is allowed to contain. It can include constraints and other measures that limit the format, type, and amount of data entered.
User-defined integrity

User-defined integrity involves the rules and constraints created by the user to fit their particular needs. Sometimes entity, referential, and domain integrity aren’t enough to safeguard data. Often, specific business rules must be taken into account and incorporated into data integrity measures.

The importance of data integrity in protecting yourself from data loss or a data leak cannot be overstated: in order to keep your data safe from outside forces with malicious intent, you must first ensure that internal users are handling data correctly. By implementing the appropriate data validation and error checking, you can ensure that sensitive data is never miscategorized or stored incorrectly, thus exposing you to potential risk. An assortment of factors can affect the integrity of the data stored in a database.

A few examples include the following:

Human error

When individuals enter information incorrectly, duplicate or delete data, don’t follow the appropriate protocols, or make mistakes during the implementation of procedures meant to safeguard information, data integrity is put in jeopardy.
Transfer errors

When data can’t successfully transfer from one location in a database to another, a transfer error has occurred. Transfer errors happen when a piece of data is present in the destination table, but not in the source table in a relational database.
Bugs and viruses

Spyware, malware, and viruses are pieces of software that can invade a computer and alter, delete, or steal data.
Compromised hardware

Sudden computer or server crashes, and problems with how a computer or other device functions are examples of significant failures and may be indications that your hardware is compromised. Compromised hardware may render data incorrectly or incompletely, limit or eliminate access to data, or make information hard to use.

Risks to data integrity can easily be minimized or eliminated by doing the following:

  • Limiting access to data and changing permissions to restrict changes to information by unauthorized parties
  • Validating data to make sure it’s correct both when it’s gathered and when it’s used
  • Backing up data
  • Using logs to keep track of when data is added, modified, or deleted
  • Conducting regular internal audits
  • Using error detection software
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Intelsoft Services is a specialized BI & Big Data consulting firm focused on data workflow optimization, data profiling, data integration solutions, data migration, data analytics and data mining through its innovative & customized business & software solutions using cutting edge advanced technologies.

  • Toronto, ON
  • +1 289 512 2199
  • info@intelsoft.ca
    support@intelsoft.ca
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