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Data lakes are like the junk drawers of the data world - they accumulate all kinds of structured, semi-structured, and unstructured data from across an organization. Customer records, sensor logs, social media posts, audio transcripts, you name it. While this "save everything" approach allows companies to retain data for future analysis, it also creates huge headaches when it comes to actually making sense of all that information. Just like cleaning out an overflowing junk drawer, getting a data lake in order takes some work.
The first step is getting an inventory of what exactly resides in your data lake. This could involve visualizing relationships between datasets, profiling data to understand types and distributions, and adding metadata like tags and documentation. Simply understanding what exists can help uncover redundant, obsolete and trivial (ROT) data that can be removed. The next phase focuses on transforming all those disparate sources into a more consistent structure. This could mean normalizing date formats, parsing text files, mapping columns, or joining related tables. Standardization is key for enabling integration across datasets.
Text data often ends up in an organization's data lake without much structure or context. Transcripts from customer support calls, notes from sales meetings, social media posts - all the unstructured text from across a business gets dumped into the lake to deal with later. But raw text files can be unwieldy beasts to wrangle into a usable form.
Before analysis, these free-flowing text documents need to be whipped into shape. This starts with extracting the sections of text that are relevant to the analysis goals. Keyword searches, named entity recognition, and topic modeling can help pull insightful excerpts from longer documents. The next step is transforming the messy text into a more structured format. This might involve splitting text into semantic units like sentences or paragraphs. It could also mean parsing text to identify key elements like dates, names, or addresses.
Tools like natural language processing and optical character recognition can aid this process. But a human touch is still required for things like resolving ambiguities or validating data formats. The end goal is getting the text into a consistent schema that designates metadata like author, date, relevant topics, and so on. This facilitates joining the transformed text with structured data from other sources.
Data scientists who have wrangled wild text emphasize the need for iteration. "It"s never a one-and-done process," says Michelle Zhou of StarSchema. "As you work with the text, you"ll uncover new needs for normalization, parsing, and structuring." The key is balancing automation with manual tuning to improve accuracy over successive passes. Zhou also advises starting with a sample before automating across all text files. "That allows you to refine based on real examples from your data before scaling up."
Audio transcripts can unlock a treasure trove of insights - but only if you can interpret them. Raw speech-to-text outputs are like a bowl of word salad. Making sense of these tangled transcripts requires reorganizing the unstructured mess into digestible bite-sized pieces of information.
Transcription tools convert speech to text through automatic speech recognition (ASR). But humans don"t speak with perfect grammar or clear meaning. We use shorthand, interrupt each other, change topics mid-sentence. "ASR accuracy has gotten amazingly good," notes Samuel Lee of ParseThis. "But machines still struggle with the nuance and fluidity of human speech."
Lee explains how speech transcripts need significant cleanup to become usable data. "We utilize a workflow combining ASR with optimization algorithms and human-in-the-loop tuning to improve transcript readability," says Lee. This process reorganizes incoherent passages, corrects mistakes, timecodes speaker changes, and extracts key details.
Streamlining scattered speech into logical sections is essential for analysis. "We break transcripts into semantic chunks by topic to make them more navigable," Lee explains. This allows users to pinpoint discussions around relevant subjects across a lengthy conversation.
Topic tagging transcripts also enables integrating the messy qual data with structured databases. "Linking transcript excerpts to categories like "product feedback" or "pricing" lets you incorporate speech analytics into quantitative analysis," says Lee.
But the most crucial step is getting the right human insight. "Domain experts annotate transcripts to pull out the signals in the noise based on what matters for the business goals," says Lee. Without this manual review, crucial nuances around sentiment, intent and meaning would be missed.
Unstructured data is the wild west of the analytics world. Unlike neatly formatted tables and databases, unstructured information is loose, messy, and difficult to rein in. Customer emails, social media posts, audio recordings, image files - this motley mix of formats accounts for over 80% of data for most organizations. And this data deluge will only increase as more interactions and transactions happen online.
"We were drowning in a sea of unstructured data," says Anjali Sharma, Chief Analytics Officer at Kinetic Insights. Product reviews, call center notes, sales meeting transcripts - relevant insights were buried across a slew of text-heavy documents and audio files from different departments. "Our analysts wasted tons of time just searching for the right information," explains Sharma, "And once they found it, making sense of the unstructured content was a whole other battle."
Sharma knew they needed to lasso all this qualitative data into an organized structure for easier access and analysis. This began by taking inventory of existing unstructured sources and identifying types of textual or verbal content that could offer valuable signals. Next came a process of enrichment, tagging files with metadata like author, date, department, and subject matter.
They utilized entity extraction to pull out key names, places, and dates within documents. Sentiment analysis classified tone and emotion of text passages. Topics models clustered content into conceptual categories. Audio and video transcripts were time-stamped to designate speakers and split into topical sections.
"The enrichment phase was hugely time-intensive thanks to the manual effort of our data science team," notes Sharma. "But their human oversight ensured we captured nuanced context that machines would miss." This set up the unstructured data for integration with their structured analytics. Text excerpts and transcript snippets could now be filtered, queried, and correlated based on metadata tags and classifications.
Making sense of the audio anarchy inundating organizations is no easy feat. Call center recordings, customer service conversations, sales pitches, conference presentations - audio data contains a treasure trove of insights, but finding the relevant nuggets can be like mining for diamonds in a vast ocean.
"We record thousands of hours of customer interactions every month," explains Aditi Sengupta, speech analytics lead at TeleCom Corp. "Our analysts were overwhelmed trying to manually comb through the haystack to find key phrases about brand sentiment or product feedback."
Just transcribing the audio was not enough. Speech to text tools spit out messy, inaccurate transcripts riddled with transcription errors and incoherent passages. "We needed to optimize both the automated transcription and the human analysis process," notes Sengupta.
Revamping their workflow required a focus on structure and navigation. Sengupta"s team developed an interface linking audio recordings to enriched transcripts synced by speaker, topic, and sentiment. Analysts can now efficiently scan transcriptions, click on relevant sections, and play matching audio - no more fruitless scanning and scrubbing.
Topic modeling provides a crucial assist by clustering transcribed text into conceptual categories. "Grouping content by subject area makes it much easier to identify customer pain points around billing, tech support, or product experiences," explains Sengupta.
But technology alone is not enough to distill insights from audio chaos. "The human analyst ultimately connects the dots in ways machines can't," says Sengupta. Her team annotates transcripts to catch sarcasm, emotion, and intent - key context that algorithms miss.
This hybrid approach has paid dividends. Sengupta cites discovering prevalent complaints around unintuitive product menus by mining call center transcripts. "We completely revamped the interface based on what we extracted from the audio data. Customer satisfaction improved dramatically."
Audio insights have also led to new sales campaign messaging and agent training programs. But Sengupta stresses the importance of selectivity. "You'll go mad sifting through every minute. Focus on extracting key themes and segments that align with your business objectives."
Dirty data is the scourge of analytics. From irregular formatting to inaccurate entries, messy malformed data can torpedo even the most sophisticated models. "We"d run these beautifully engineered algorithms and get bizarre results," recalls Joanna Carson, data science manager at Datalogz. "It took months before we realized the models were garbage in, garbage out because our data was full of problems."
Typos, missing fields, outliers - dirty data comes in many forms. The first step is identifying what types of anomalies exist. This requires a keen understanding of expected formats and distributions for each dataset. "We built validation checks into the pipelines ingesting the data to catch issues early," explains Carson. Her team also utilized visualizations to profile data and spot outliers. Analyzing patterns helped uncover systemic data failures versus one-off typos.
Fixing dirty data often takes a combination of automation and human oversight. Carson enlisted data engineers to develop scripts tackling reusable problems like parsing irregular date strings or imputing missing ZIP codes. But many issues required manual reviews by subject matter experts. "Spelling mistakes in client names or illogical values needed human eyes," says Carson. Her team developed a ticketing system for data stewards to investigate anomalies flagged by the validation checks.
This hybrid approach formed their data debugging workflow. Carson emphasizes the importance of iteration. "You have to monitor and tune the checks and fixes over multiple passes on the data to improve integrity." Onboarding new data sources meant new debugging challenges, requiring continual refinement of error handling scripts. "It"s an ongoing process, not a one-time fix," stresses Carson.
Carson urges organizations to prioritize data testing just like software testing. "You have to validate with the same rigor as user testing a new app feature before launch," she says. "Letting bad data flow into products is just as unacceptable as bugs in code." She speaks from experience. Early in her career, Carson's team spent months developing a demand forecasting algorithm before realizing the historical sales data had unit measurement errors making their predictions wildly inaccurate. "Now I instill in my team the mindset of data paranoia," she says. "Verifying quality is the foundation on which everything else gets built."
Spreadsheets and databases may seem neat and tidy, but hidden chaos often lurks within these supposedly structured data sources. From inconsistent headers to fragmented records, tabular data routinely falls victim to entropy. Left unaddressed, these small corruptions accumulate into larger problems that derail analytics. "We kept getting nonsensical results when analyzing our sales numbers," recalls Amar Patel, data governance lead at Acme Corp. "Turns out there were five different abbreviations for the California region scattered across our system."
Such headaches highlight the need to periodically tidy tabular data stores. "You have to proactively fix issues before they cascade," urges Wendy Shields, Information Architect at DataWise. "One typo multiplies to thousands of bad rows once it gets propagated." Shields speaks from experience, having previously coordinated a massive data harmonization initiative at Telco Inc. integrating customer records from dozens of legacy systems rife with conflicts.
The first step in any tidying task is taking stock of what types of inconsistencies exist and how messy the data truly is. Running frequency distributions, sorting on fields, and profiling for outliers can uncover areas of disorder. "We built Tableau dashboards for visual anomaly detection - things like bars charts of region names or date ranges that made data issues pop out," explains Shields.
Automation can then resolve common problems at scale, like standardizing state abbreviations or correcting invalid ID codes. "We invested in data quality tools specifically for tabular data that automated 60-70% of fixes," says Patel. But, he notes, some tidying required manual reviews by subject matter experts to, for example, identify which of 20 slightly different customer segment labels to standardize on.
Ongoing vigilance is crucial, stresses Shields. "We instituted "tidy time" every quarter to cleanse key systems." She also emphasizes thinking holistically. "Fixing problems in isolation can just shift issues elsewhere." For example, deduplicating customer records separately in each satellite system could still leave duplicates across the enterprise.
Speech data often enters organizations as a scattered mess of call transcripts, meeting minutes, presentations, and customer conversations. While this qualitative information offers a rich trove of insights around sentiment, needs, and experiences, gleaning intelligence from scattered speech can feel like finding needles in a haystack.
"We record thousands of hours of sales and support calls each month," explains Priya Capel, analytics manager at TalkCo. "Our analysts wasted so much time trying to manually piece together insights around customer pain points or agent performance. Relevant conversations were buried across hundreds of call transcripts."
To streamline analysis, Capel"s team focused on structuring the speech data for easier navigation. They utilized speech analytics software to transcribe audio recordings and sync transcripts to the original media. To aid discovery, the text was enriched with speaker IDs, timestamps, and topical tags based on keyword searches.
"We built a searchable interface linking key transcript excerpts to the corresponding audio," explains Capel. "This lets analysts efficiently scan transcripts, click on relevant passages, and play the matching section of the call."
Topic modeling provides another navigate assist by clustering transcripts into conceptual buckets. "We can filter and search by subject area to quickly uncover discussions around billing, technical support, or product feedback," notes Capel.
But technology alone is not enough to extract signals from scattered speech. "The human analyst ultimately connects the dots in ways machines can't," stresses Capel. Her team reviews transcripts to annotate tone, sentiment, and intention - crucial context that algorithms miss.
This workflow has enabled fast discovery of customer pain points. "We identified widespread complaints about unintuitive account management portals based on call center conversations," explains Capel. "That led us to overhaul the interface."
Streamlining speech analysis has also informed agent coaching programs. "We can efficiently surface great examples of empathetic and clear explanations from the best reps to share as training models," says Capel.