Content Map
1. Unclear Business Problem Identification
2. Data Collection
3. Getting Data from Multiple Sources
4. Data Security and Privacy
5. Data Cleansing
6. Data Understanding
7. Lack of Data Scientists with Required Skills
8. Communication of Results to Non-Technical Stakeholders
9. High Cost
10. Effective Collaboration
Acknowledging powerful applications of data science in real life, businesses worldwide have increasingly invested in this field over the past few years. From product recommendation systems in e-commerce to personalized treatment plans for patients, data science is transforming businesses and customer satisfaction.
Do you know that, to pursue their ambitious data-driven goals, in 2019, Google acquired Looker, a startup specializing in data analytics? Additionally, Deloitte Access Economics predicts 76% of enterprises will be augmenting their investments in data analytics.
However, all these do not necessarily mean that every company could successfully drive growth via data science. Quite to the contrary, they often confront multiple challenges in realizing data science potential.
So, what are these data science challenges? How do businesses deal with them?
Now, sit back, relax, and let’s dig into the difficulties business owners and data scientists are facing and solutions to overcome them.
Data science integrates mathematics, statistics, specialized coding, advanced analytics, artificial intelligence (AI), and machine learning (ML), with expertise in specific subject areas, to analyze and interpret large data volumes. These insights can facilitate decision-making and strategic planning as well as improve overall business performance.
The rapid proliferation of data sources and data itself has transformed data science into one of the swiftest-growing fields across all industries. It’s applied in the telecom industry, digital marketing, fintech, and e-commerce to optimize prices, improve the customer experience, and more.
Apart from traditional databases, there are numerous new sources to get valuable data, such as social media, sensors, and internet-connected devices.
Besides, the data science lifecycle encompasses diverse roles, tools, and procedures, empowering analysts to derive valuable insights. Generally, data science projects involve the following phases:
Despite working in this field, a data scientist isn’t necessarily held directly accountable for every facet of the data science lifecycle. Theoretically, their responsibilities revolve around building predictive models utilizing advanced mathematical concepts, statistics, and programming tools.
Nonetheless, in reality, there exist misconceptions regarding their roles. In most companies, data scientists are tasked with retrieving and cleaning data, building models, and presenting their findings in business-friendly terms. Unfortunately, data scientists face unique real-life data problems at each phase of their work frequently, impeding their progress and causing frustration within data teams.
More on this in the next section!
It’s crucial for companies to initially analyze the business difficulties they intend to address with data science solutions. Employing a mechanical approach of identifying data sets and analyzing data before clearly understanding the business issue would do more harm than good, especially when data science is applied for informed decision-making.
Even with a defined purpose, there is no point in implementing data science if its expectations do not align with the ultimate business goals.
Thus, crafting a well-structured workflow is needed to locate a suitable use case. Constructing such workflows necessitates collaborative efforts across all departments and the formulation of a checklist to enhance problem recognition.
The initial stage of any machine learning or data science project involves locating and gathering the required data assets. Nevertheless, it’s not easy for businesses and data scientists to access appropriate data. This directly affects their capacity to build robust ML models.
But what makes data acquisition such a challenge in a world full of it?
The primary issue lies in organizations accumulating extensive data without evaluating its utility. This arises from a common fear of missing out on valuable insights and the availability of affordable data storage. Consequently, organizations may feel overwhelmed with unnecessary data.
In this instance, instead of accumulating extensive data without evaluating its utility, organizations should invest time in evaluating its relevance and quality. Conduct data audits or quality assessments to discard data that doesn’t contribute to the objectives. Plus, partner or collaborate with organizations or individuals who already have access to the data you need. This step eliminates the effort and time required to collect data from scratch and enables access to specialized data sources that would otherwise be difficult to acquire.
The third obstacle lies in the abundance of data sources, making it hard to locate the appropriate data.
Enterprises gather data about their customers, sales, employees, and more, utilizing various tools, software, and CRMs. The sheer volume of data inundating organizations can lead to issues regarding data consolidation and management.
In this case, organizations should establish sophisticated virtual data warehouses equipped with a centralized platform, unifying all data sources in one place. This central repository allows for data modification or manipulation to cater to a company’s requirements and enhance its efficiency. It can also help data scientists save enormous time and effort.
Once suitable data sets are identified, the next challenge is gaining access. However, mounting privacy and compliance concerns have made it increasingly difficult for data scientists to access necessary data, especially confidential data and sensitive data.
Furthermore, the extensive migration to cloud data management environments has rendered cyberattacks increasingly common in recent years. This has led to heightened security measures and regulatory demands. These factors have collectively made it challenging for most data scientists and ML engineers to obtain the data sets they require.
Especially when companies give other parties access to their data sets, they will face the additional challenge of ensuring ongoing security and compliance with data protection regulations, such as GDPR. Neglecting either aspect could result in severe financial penalties and costly, time-consuming audits by regulatory authorities.
To overcome data security threats, businesses should enforce robust data governance policies and practices, including data access controls, data encryption, and data anonymization techniques. Data catalogs can also help to govern data access by permitting administrators to grant or restrict access to specific data sets based on user roles and permissions. This ensures that data scientists have access only to the data they necessitate while upholding data security and compliance.
Not all data collected is valuable for the model. Some may be incorrect, corrupted, incorrectly formatted, and duplicated, which need removing.
Data scientists allocate most of their time to preprocessing data sets, ensuring consistency before entering data analysis. This is often considered the worst part of a data scientist’s responsibilities, such as cleaning data, removing outliers, encoding variables, extracting unstructured data, etc.
Notably, according to MIT Sloan Management Review, companies can lose up to 25% of their revenue due to the expensive nature of cleaning useless data.
However, models must be built using clean, high-quality data; otherwise, machine learning models may learn wrong patterns, leading to inaccurate predictions.
What’s the solution, then?
The first solution involves augmented analytics adoption, which leverages machine learning and artificial intelligence to help with data preparation, potentially automating specific facets of data cleansing. The process saves data scientists substantial time without compromising productivity levels.
Additionally, data governance presents another good solution, referring to a set of processes to manage data assets within an organization. Data scientists can also harness numerous modern data governance tools to cleanse, format, and uphold data set accuracy.
IBM Data Governance, OvalEdge, Collibra, Truedat, Informatica, Alteryx, and Talend are worth your consideration as top data governance tools.
Even after identifying and gaining access to specific data sets, data scientists still face considerable difficulty building meaningful models. They often find themselves pondering over seemingly simple questions, such as:
While these questions appear straightforward, getting answers proves challenging. This is because, within organizations, data sets often lack designated ownership, making it difficult to identify who does this or that.
Data asset documentation comes in handy in this situation. By establishing written definitions for each column in every table within the data warehouse, the productivity of data scientists can experience a substantial boost. As tedious as this may seem, it undeniably takes less time than just letting data assets undocumented, wasting a lot of time.
Plus, modern data documentation solutions also feature automation, where defining a single column in a table propagates the definition to all other columns with similar names in other tables.
The talent shortage is one of the significant data science problems encountered by companies, where they often grapple with finding the right data team having both profound knowledge and domain expertise. In addition to a comprehensive understanding of data science algorithms, ideal candidates are expected to possess insights into the business perspective.
Ultimately, the success of a data science project depends on the company’s ability to narrate its business story through data. As a result, another pivotal skill to seek in analysts and scientists is the ability of storytelling through data, coupled with problem-solving potential.
Considering that not all departments are fluent in the language of data, an ideal team should be able to communicate effectively with other teams. Given the distinct priorities and workflows of various teams, achieving alignment among all teams is of great significance. A potential data scientist should know how to translate technical intricacies into easy-to-understand matters, enabling business owners to grasp them effortlessly.
However, finding such a team is obviously challenging. Reaching out to a data science company represents a viable option, as they not only possess the requisite technical expertise but also comprehend the business dimension of the project and are willing to commit to it.
The end goal of data science is to steer and enhance decision-making within organizations. That’s why the work of data scientists should harmonize seamlessly with the business strategy. This requires them to communicate their findings to non-technical stakeholders, such as business executives and managers, who often lack knowledge about the data science tools and mechanisms behind models.
These non-technical stakeholders have to base their decisions on data scientists’ explanations. Hence, if data scientists cannot articulate how their model will influence organizational performance, their solution may not be realized.
To address communication hurdles in data science, data scientists should hone their data storytelling capabilities via visualizations. Moreover, organizations should precisely and clearly define definitions for key business terms and KPIs, ensuring that all teams share a common understanding of these metrics. Data scientists are able to align their analyses with business objectives better and communicate their results more effectively.
It’s obvious that forming in-house machine learning teams, managing projects, and constructing and deploying ML tools is a costly endeavor. This expense can be prohibitive even for larger enterprise-level firms, particularly when projects fail to yield anticipated results.
Smaller and mid-sized organizations might feel that harnessing data and ML for their business is beyond their reach due to the cost associated with assembling their ML teams (logistics, cost, expertise, etc.) Still, this is not entirely accurate.
Although smaller firms may encounter substantial barriers if they seek to establish their ML teams, there exist multiple tools and solutions available in the market that enable them to fully outsource their ML projects without compromising the ML model quality.
Data scientists and data engineers usually have to collaborate on the same projects within an organization. Keeping communication channels robust is imperative to prevent potential conflicts and ensure the compatibility of both teams’ workflows. Alternatively, companies can appoint a Chief Officer to oversee whether both departments operate in harmony.
Navigating huge data sets and addressing data science challenges has never been easy. Organizations may face problems identifying business issues, collecting data, data security, and cleaning data. They also find it difficult to efficiently communication with non-technical stakeholders.
Among the available solutions mentioned in this article, one noteworthy approach is to outsource to a dedicated big data/ML engineering platform, which can yield superior outcomes. These vendors have unrivaled expertise to push your business further and, at the same time, allow you to stay focused on your business strategy.
Here at Neurond, we offer professional data science consultation and comprehensive support through all stages of implementation. Contact us now for expert guidance and assistance in successfully harnessing the power of data science for your organization.
Content Map What is the Role of a Data Scientist in Business? How to Hire Data Scientists Effectively: A Step-by-step Guide Skills and Qualifications to Look for in a Data Scientist How Much Does It Cost to Hire Data Scientists? Challenges of Recruiting Data Scientists and How to Overcome Them Final Thoughts Data has become […]
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