The data security challenges are not linked to the company's lack of AI technologies

For every company, data is both an opportunity and a responsibility. Horror reports of massive data breaches make the news quicker than suicides. They may result in dramatic stock price losses, a barrage of bad coverage, and widespread mistrust of the business.

The assault on Yahoo in 2013 (3 billion consumer identities compromised), Mariott's continuing data breach involving 500 million consumers between 2014 and 2018, and the more recent Sephora problem in Southeast Asia of an undisclosed extent are only a handful of the most high-profile data breaches.

Implementing a BI initiative necessitates significant time and financial commitments. As a consequence, it is crucial to ensure that it is carried out flawlessly. We illustrate certain main factors to bear in mind when applying market intelligence.

Much of the cybersecurity hysteria leads businesses to believe that digital management is too difficult to manage on their own, yet if they invest in all of these trendy technologies, they would be able to secure their data against the most recent cyber attacks. It also contributes to the belief that silver bullets exist that will offer a one-stop solution to all potential challenges at once and that obtaining such magic medicines requires ever-increasing budgets. The three biggest issues that can jeopardize your data protection aren't linked to your portfolio's lack of AI technologies.

3 Major Challenges in data security

The first issue is that IT departments are understaffed and undertrained. Most of the IT defense departments are understaffed, which is a bigger concern. For example, in small companies, IT administrators often tend to wear several hats — in some instances, there is only one IT expert responsible for anything from managing service outages to addressing consumer desktop problems to safeguard confidential data. Also, as per BI experts, bigger organizations working with Power BI often go for Power BI Training, It teaches the best practices around data and security before team start working on that. Also, the IT department is overworked because they do not have time to investigate what types of confidential data they retain or devise a scheme to secure it.

Financial limitations are the second problem. Many companies are reluctant to commit a substantial portion of their resources to recruit new IT security staff or educate existing workers on sustaining computer security. As a consequence, it seems that purchasing a few solutions that cybersecurity providers say can secure data against various data protection risks is much cheaper and faster, which leads to the next issue:

Spending money on ineffective methods is the third challenge. Companies often have no idea what sort of classified information they have, where it is processed, or if it is overexposed. They do, though, purchase a range of tools to «protect» it. They later discover that the technologies they purchased in haste do not match up to the vendors' commitments or satisfy their own needs. According to a Cybersecurity Ventures report, global investment in cybersecurity goods and services hit $120 billion in 2018. Before 2021, this figure would have surpassed $1 trillion, representing an 88 per cent increase in total cybersecurity expenditure!

BI implementation is the future despite of few challenges

There is no such thing as a protected enterprise, and there is no one-size-fits-all approach to guarantee data security. However, since the benefits outweigh the drawbacks, data-rich BI and CRM systems are here to remain, and businesses must find cost-effective ways to ensure their security.

Carefully pick the vendor: In recent months, the leading IT service companies have been consolidating, possibly to provide their clients with a one-stop solution. However, as part of the BI installation, you can expect to use various presentation layers. While one platform might have outstanding monitoring functionality, it may neglect excellent dashboard capabilities. You might still be ignorant of strategies that reduce the time it takes to introduce BI. Shoppers Stop, for example, bought a supermarket data model from a boutique provider as part of its BI deployment. The data model effectively consisted of a blueprint of fields for the data warehouse, storage types, and a list of foreign indicators, many of which were highly useful to the enterprise. Therefore, carefully analyze each solution — if necessary, execute a proof of concept (POC) on your own data to validate criteria such as results.

Data collection: Believing that data can be collected quickly from online transaction processing (OLTP) systems may trigger delays in BI implementation. Examine each area closely when planning the collect, convert, and load (ETL) procedures, testing how the data is collected and evaluating update mechanisms for the field. People who work on classified piles of data are known as "Excel jockeys" in any organization. Their data structures must be obtained and standardized, and all of these Excel sheets must be converted into tables once and for all. The departmental data mart is more advanced because it receives its data from the enterprise's key apps. If you don't make a deliberate attempt to classify those people, you could end up feeding your data mart from your BI stack rather than the other way around. You can also see how the BI project will handle the data mart's functions since this would save you from managing yet another program.

Set the correct expectations: It's impossible to assume a BI implementation to work miracles in terms of data processing and automation of current reporting processes overnight. Instead, a BI initiative can be launched with key client priorities in mind, such as growing the consumer base and loyalty, widening established markets, and the sales, to name a couple. It's critical to think about importance, accuracy, continuity, and timeliness before starting every project module.

Final Takeaway

To have everybody on the same page, start with the following steps: When gathering data for BI deployment, it's essential to make sure everybody is on the same page when it comes to terminology. The same word can be defined differently by different departments inside the same organization. Also, a simple word like "margin" will cause the acquisition and finance departments to come up with various figures. Since the enterprise BI stack's concept is to provide a compatible version of the facts, having a single version of the language is critical. As a way of rendering tasks more manageable, the project must be shared.